Determining next best action using graded escalation triage alogrithm

ABSTRACT

A method may include collecting data indicating a number of patients diagnosed with one or more chronic diseases in a geographic location based on epidemiological attributes of the patients. The method may include collecting data that indicates a number of patients that are diagnosed with the chronic diseases in a practice of a physician. The method may include comparing the number of the patients diagnosed with the chronic diseases in the practice of the physician to the number of patients diagnosed with the chronic diseases in the geographic location. The method may include generating a comparison result that may indicate whether the number of patients diagnosed with the chronic diseases in the practice of the physician is greater than or less than the number of patients diagnosed with the chronic diseases in the geographic location. The method may include providing an alert to a care-provider that includes the comparison result.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.16/217,888 filed Dec. 12, 2018 which claims the benefit of and priorityto U.S. Provisional App. No. 62/597,818 filed Dec. 12, 2017. The Ser.No. 16/217,888 application and the 62/597,818 application is eachincorporated herein by reference.

FIELD

The embodiments discussed herein are related to determine the Next BestAction for an individual leveraging graded escalation triage algorithmfor under diagnosis using big data.

BACKGROUND

Unless otherwise indicated herein, the materials described herein arenot prior art to the claims in the present application and are notadmitted to be prior art by inclusion in this section.

Chronic diseases have a significant impact on patients, offices ofphysicians, and hospitals. For example, on average forty five percent ofMedicare patients will visit an emergency room in a twelve month period.Each visit to the emergency room may cost a patient around $3000 andconsumes the time of both the patient and physicians that work in theemergency room. Similarly, the personal physician of the patient willperform a follow up visit to make sure the chronic disease is beingproperly treated after the visit to the emergency room by the patient.Prediction of onset of chronic diseases, and their management withoutconducting an in-person physician visit and/or without being overlyburdensome on the patient and/or physician is a complex and expensiveissue.

One aspect that makes management of chronic diseases, and prediction ofan impact chronic diseases will have on a patient, so complex is thelarge number of physical, mental, and demographic factors that may betaken into account. Similarly, capturing data related to the physicaland mental factors that impact managing chronic diseases and predictingthe impact of chronic diseases on a patient is typically an arduous anddrawn out process, which adds layers of complexity to managing chronicdiseases and predicting the impact of chronic diseases on a patient.Likewise, existing methods for combining the available data are notsimple, efficient, practical, nor easy to use, which adds yet anotherlayer of complexity to managing chronic diseases and predicting theimpact of chronic diseases on a patient.

Additionally, testing each patient that has one or more chronic diseasesfor different chronic diseases or changes to the previously diagnosedchronic diseases may be exorbitantly expensive to conduct. In somescenarios, the cumulative cost of testing every patient to detect theunhealthy cases may be so exorbitant that it outweighs the benefit, interms of an increased quality adjusted life year, that it provides tothe average patients.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one example technology area where some embodiments describedherein may be practiced.

BRIEF SUMMARY OF SOME EXAMPLE EMBODIMENTS

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential characteristics of the claimed subject matter, nor is itintended to be used as an aid in determining the scope of the claimedsubject matter.

Some embodiments described herein generally relate to under-diagnosisdetection using big data.

In an example embodiment, a method to evaluate relative risk ofunder-diagnosis of a patient may include collecting data that indicatesa number of patients diagnosed with one or more chronic diseases in ageographic location based on epidemiological attributes of the patients.The method may include collecting data that indicates a number ofpatients that are diagnosed with the one or more chronic diseases in apractice of a physician. The method may include comparing the number ofthe patients diagnosed with the one or more chronic diseases in thepractice of the physician to the number of patients diagnosed with theone or more chronic diseases in the geographic location. The method mayinclude generating a comparison result that may indicate whether thenumber of patients diagnosed with the one or more chronic diseases inthe practice of the physician is greater than or less than the number ofpatients diagnosed with the one or more chronic diseases in thegeographic location. The method may include providing an alert to acare-provider that includes the comparison result.

In another example embodiment, a system to evaluate relative risk ofunder-diagnosis of a patient may include a first database, a seconddatabase, a memory, and a processor. The first database may include datathat indicates a number of patients that are diagnosed with one or morechronic diseases in a geographic location based on epidemiologicalattributes of the patients. The second database may include data thatindicates a number of patients that are diagnosed with the one or morechronic diseases in a practice of a physician. The memory may beconfigured to store the first database and the second database. Theprocessor may be coupled to the memory and may be configured to performexecutable operations. The operations may include comparing the numberof the patients diagnosed with the one or more chronic diseases in thepractice of the physician included in the second database to the numberof patients diagnosed with the one or more chronic diseases in thegeographic location included in the first database. The operations mayinclude generating a comparison result that indicates whether the numberof patients diagnosed with the one or more chronic diseases in thepractice of the physician is greater than or less than the number ofpatients diagnosed with the one or more chronic diseases in thegeographic location. The operations may include providing an alert to acare-provider that includes the comparison result.

These example embodiments are mentioned not to limit or define thedisclosure, but to provide examples to aid understanding thereof.Additional embodiments are discussed in the Detailed Description, andfurther description is provided there. Advantages offered by one or moreof the various embodiments may be further understood by examining thisspecification or by practicing one or more embodiments presented.

BRIEF DESCRIPTION OF THE FIGURES

These and other features, aspects, and advantages of the presentdisclosure are better understood when the following Detailed Descriptionis read with reference to the accompanying drawings.

FIG. 1 is a block diagram of an example chronic care management system;

FIG. 2 is a flow diagram of an example method to generate a total healthscore of a patient;

FIG. 3 is a graphical representation of a number of Medicare patientsthat are not hospitalized within a year of providing a patient generalhealth input;

FIG. 4 is a graphical representation of a number of Medicare patientsthat do not die within a year of providing a patient general healthinput;

FIG. 5 is a graphical representation of how various factors may impactmanagement of chronic diseases;

FIG. 6 is a graphical representation of anatomically detailed humanavatars;

FIG. 7 is a flow diagram of an example method to predict a likelihood ofa patient experiencing an acute event in the near future;

FIG. 8 is a flow diagram of an example method to evaluate relative riskof under-diagnosis of a patient;

FIG. 9 is a flow diagram of an example method to evaluate and stratify achronic care burden of a patient;

FIG. 10 is a flow diagram of an example method to generate a health riskscore of a patient;

FIG. 11 is a flow diagram of an example method to evaluate and stratifya lifestyle health compliance of a patient;

FIG. 12 is a block diagram illustrating a physical predictive model ofthe physical health of a patient in which the patient may be in one oftwo states;

FIG. 13 is another block diagram illustrating a physical predictivemodel of the physical health of a patient in which the patient may be inone of three states;

FIG. 14 is a block diagram of an example computing device;

FIG. 15 is a flow diagram of an example method to generate a physicalpredictive model of a physical health of a patient; and

FIG. 16 is a flow diagram of an example method to generate a mentalpredictive model of a mental health of a patient,

all arranged in accordance with at least one embodiment describedherein.

DETAILED DESCRIPTION

Several factors may affect management of chronic diseases and predictingan impact of chronic diseases on a patient. For example, there may bepatient level factors (e.g., personality, psychological state, values,and/or preferences of the patient) along with environmental/communityfactors (e.g., psychological, social, and/or economic state of thecommunity) that may have an impact. Likewise, there may be biological,physiological, and/or nonmedical factors of the patient that may have animpact. Clinical symptoms of the patient may be affected by thepersonality and/or psychological state of the patient; psychologicalstate of the community; and/or the biological and/or physiological stateof the patient. Additionally, physical and psychological function of thepatient may be impacted by the clinical symptoms of the patient; thepersonality and/or psychological state of the patient; and/or the socialand/or economic state of the community.

Similarly, general health perceptions of the patient may be affected bythe physical and psychological function of the patient; the valuesand/or preferences of the patient; and/or the social and/orpsychological state of the community. Likewise, overall quality of lifeof the patient may be affected by the general health perceptions of thepatient; the values and/or preferences of the patient; the social and/orpsychological state of the community; and/or various nonmedical factors.Some of these factors may vary depending on the age, gender, race,ethnicity, geographic location, and/or other demographic factors of thepatient.

There are also several metrics that are available to determine thedifferent factors discussed above. For example, the metrics may includea quality of wellbeing scale self-administered (QWB-SA), a health andactivities limitation index (HALex), a short form six dimension (SF-6D),a health utilities index mark 2 (HUI2), a health utilities index mark 3(HUI3), a euroQO1 five dimension (EQ-5D), or any other acceptable metricfor determining one or more of the factors that may affect management ofchronic diseases and predicting an impact of chronic diseases on apatient.

Management of chronic diseases and predicting the impact of chronicdiseases on the patient in a simple, efficient, and easy to use mannermay be achieved by combining patient input that provides meaningfulinformation related to the current health of the patient with additionaldata that is easy to measure, is credible, and can be readily obtainedby a physician or their office. The combination of the different piecesof information may provide simple and efficient methods for determininga short-term health compliance (SHC) score, a chronic care burden (CCB)score, a lifestyle choice compliance (LCC) score, and/or a total healthscore of the patient. Additionally, combining the different pieces ofinformation may provide simple and efficient methods for determining arisk of under diagnosis of one or more chronic diseases within apractice of the physician. Additionally, a large portion of theinformation may be obtained without the patient having to visit thephysician in person.

Furthermore, combining the different pieces of information may provideboth valid and reliable results that are easily understood by physiciansand other people who use the results. Likewise, the results may bemeasurable over time and may be measureable for different geographiclocations and/or demographics of patients. Additional factors that maybe considered may include socioeconomic conditions, environmentalconditions, or public policies that may impact the patient. Likewise,providing efficient and simple methods for obtaining the differentpieces of information may reduce a standard error of patient data sincepatients may be tested more often (e.g., every two weeks) with a reducedburden on the patient.

In one embodiment, prediction of a patient with at least one chronicdisease experiencing an acute event (e.g., visiting an emergency room(ER) and/or hospitalization) in the near future (e.g., within the nextthirty to sixty days) may be performed. In some embodiments, predictionof a patient with at least one chronic disease experiencing an acuteevent in the intermediate future (e.g., in six to twelve months) may beperformed. An electronic device may receive patient general health inputin response to a general health questionnaire from a patient with atleast one chronic disease. For example, the electronic device mayreceive the patient general health input indicating whether the patientwould say that in general their health is excellent, very good, fair, orpoor. As another example, the electronic device may provide ananatomically detailed avatar to the patient to allow the patient toprovide a visual analog scale (VAS) pain scale score on a portion of theavatar related to pain being experienced by the patient. If the patientgeneral health input indicates that, in general, their health is fair orpoor, the electronic device may provide and/or administer aquestionnaire about activity limitations, such as HALex questionnaire,to the patient via a user interface of the electronic device. PatientHALex input may be received in response to the HALex questionnaire. Thepatient HALex input may indicate whether the patient has recentlyexperienced or is currently experiencing a limitation in activity. Theelectronic device may determine whether the patient is likely toexperience an acute event in the near future (e.g., in thirty to sixtydays) based on the patient general health input, the VAS pain scale,and/or the patient HALex input.

If the patient HALex input does not indicate that the patient hasrecently experienced or is currently experiencing a limitation inactivity, the electronic device may provide and/or administer a healthrelated quality of life (HRQOL) questionnaire to the patient. PatientHRQOL input may be received in response to the HRQOL questionnaire. Theelectronic device may determine whether the patient is likely toexperience an acute event in the near future (e.g., the next thirty tosixty days) based on the patient general health input, the VAS painscale, the patient HALex input, and/or the patient HRQOL input. In someembodiments, the electronic device may compare the patient HRQOL inputto chronic data (e.g., chronic data included in a center for diseasecontrol and prevention (CDC) database and/or a national health interviewsurvey (NHIS) database or other source) related to the one or morechronic diseases that impacts the patient. For example, the electronicdevice may compare the patient HRQOL input to the chronic data relatedto other patients that have similar demographic or geographiccharacteristics. The electronic device may provide the patient generalhealth input, the patient HALex input, and/or the patient HRQOL input tothe physician so that the physician can contact the patient to discussthe results of the various questionnaires or to schedule an in-personexamination.

In an embodiment, prediction of a risk of a patient with at least onechronic disease experiencing a rise in a chronic care burden associatedwith one or more chronic disease (e.g., admission to an intensive careunit (ICU) and/or hospitalization) in the intermediate future (e.g., insix to twelve months) may be performed. In some embodiments, predictionof a risk of a patient with at least one chronic disease experiencing arise in a chronic care burden associated with one or more chronicdisease in the near future (e.g., within the next thirty to sixty days)may be performed. The electronic device may receive electronic healthdata record (EHR) data from the physician of the patient. The EHR datamay include a list of chronic diseases of the patient (e.g., a patientclinic profile). Additionally, the EHR data may include a patientprofile of the patient. In some embodiments, when a short-term period isevent free, then with passage of time (almost up to two years), one maysee a gradually diminishing and quantifiable contribution of risk fromthe factors contributing to short term risk. On the other hand, withpassage of time one may see gradually increasing and quantifiablecontribution of risk from the factors contributing to the chronic careburden.

The risk of the patient experiencing a rise in the chronic care burdenassociated with chronic diseases may be adjusted based on hazards thatare included in the patient profile. For example, the patient profilemay include a hazard score which reflects the chronic care burdenassociated with patient's diseases profile and patient's socioeconomicprofile. If the patient profile suggest a hazard, the electronic devicemay provide an alert to the physician indicating that the patient hasincreasing likelihood to experience an acute event in the near future.The electronic device may determine the chronic care burden of thepatient based on the EHR data, and/or responses to questionnaires.Additionally, or alternatively, the chronic care burden may be furtherbased on a disease score obtained using a disease calculator.

In an embodiment, risk prediction can be made for an impact that thelifestyle choices of the patient may have on their chronic diseases. Theelectronic device may receive patient lifestyle input in response to ahealthy lifestyle and personal control questionnaire (HLPCQ). Thepatient lifestyle input may include data related to dietary healthchoices, dietary harm avoidance, daily routine, organized physicalexercise, and/or social and mental balance of the patient. Theelectronic device may store a database of statistically significantnumber of similar and comparable patients and using that it maydetermine the impact the various lifestyle choices of the patient mayhave on the chronic diseases of the patient. The electronic device maydetermine a lifestyle choice compliance (LCC) score based on the patientlifestyle input. Additionally, the electronic device may determine oneor more lifestyle changes (e.g., lifestyle prescriptions) that thepatient should make to reduce the impact the current lifestyle choicesof the patient have. The electronic device may provide a list of thelifestyle changes to the patient and/or the physician.

In an embodiment, a total health score of the patient may be determined.The total health score may indicate an overall health of the patient andareas of health that are a concern to the physician. The electronicdevice may determine the total health score based on the SHC score, theCCB score, and the LCC score. The SHC score may be based on theprediction of the patient experiencing an acute event in the nearfuture. The CCB score may be based on the prediction of the risk of thepatient with at least one chronic disease experiencing a rise in thechronic care burden. Also, the LCC score may be based on the predictionof the impact lifestyle choices of the patient may have on their chronicdiseases. Each portion of the total health score may be weightedequally. Alternatively, one or more portions of the total health scoremay be weighted differently than one or more other portions. The totalhealth score may provide quantified guidance to the patient and/or thephysician with regards to the short-term health, the chronic burden,and/or the lifestyle impact on chronic diseases of the patient.

In an embodiment, a possible under diagnosis of one or more chronicdiseases within a practice of a physician may be determined. Theelectronic device may receive the EHR data from the office of thephysician. The EHR data may indicate a percentage of patients that arediagnosed with one or more chronic diseases in the office of thephysician. The electronic device may also receive the chronic datarelated to one or more chronic diseases. The chronic data may indicate apercentage of patients in a similar geographic region and/or withsimilar demographic characteristics that are diagnosed with the samechronic diseases. The percentage of patients that are diagnosed with thechronic diseases that are included in the EHR data may be compared tothe percentage of patients that are diagnosed with the same chronicdiseases that are included in the chronic data. If a difference betweenthe two sets of data is outside a threshold diagnosis value range, theelectronic device may provide an alert to the physician indicating thatthe percentage of patients in the EHR data is not the same or similar tothe percentage of patients in the chronic data and the physician maywant to evaluate why the difference exists. The threshold value rangemay be adjusted based on the hazards identified in the input data.

Being able to better manage chronic diseases and predict impact ofchronic diseases on a patient may improve the quality of life of thepatient and reduce financial burdens associated with treating chronicdiseases.

FIG. 1 is a block diagram of an example chronic care management (CCM)system 100 (hereinafter “system 100”), arranged in accordance with atleast one embodiment described herein. As depicted in FIG. 1, the system100 may include an electronic device 102, a network 118, a user device124, a chronic disease database 126, and an electronic health record(EHR) database 128. Additionally, the system 100 may include one or moresensors. For example, the system 100 may include a first sensor 120 anda second sensor 122 (collectively referred to herein as the sensors 120and 122). While two sensors 120 and 122 are shown in FIG. 1, moregenerally the system 100 may include one sensor 120 or 122, may notinclude a sensor 120 or 122, or may include more than two sensors 120and 122.

The electronic device 102 may include a computer-based hardware devicethat includes a processor, memory, and communication capabilities. Theelectronic device 102 may be coupled to the network 118 to communicatedata with any of the other components of the system 100. Some examplesof the electronic device 102 may include a mobile phone, a smartphone, atablet computer, a laptop computer, a desktop computer, a set-top box, avirtual-reality device, a connected device, or other suitable electronicdevice. The electronic device 102 may include a processor-basedcomputing device. For example, the electronic device 102 may include ahardware server or another processor-based computing device configuredto function as a server. The electronic device 102 may include memoryand network communication capabilities.

The network 118 may include any communication network configured forcommunication of data and/or signals between any of the components(e.g., 102, 120, 122, 124, 126, and/or 128) of the system 100. Thenetwork 118 may be wired or wireless. The network 118 may have numerousconfigurations including a star configuration, a token ringconfiguration, or another suitable configuration. Furthermore, thenetwork 118 may include a local area network (LAN), a wide area network(WAN) (e.g., the Internet), and/or other interconnected data pathsacross which multiple devices may communicate. In some embodiments, thenetwork 118 may include a peer-to-peer network. The network 118 may alsobe coupled to or include portions of a telecommunications network thatmay enable communication of data in a variety of different communicationprotocols.

In some embodiments, the network 118 may include or may be configured toinclude a BLUETOOTH® communication network, a Z-Wave® communicationnetwork, an Insteon® communication network, an EnOcean® communicationnetwork, a wireless fidelity (Wi-Fi) communication network, a ZigBeecommunication network, a HomePlug communication network, a Power-lineCommunication (PLC) communication network, a message queue telemetrytransport (MQTT) communication network, a MQTT-sensor (MQTT-S)communication network, a constrained application protocol (CoAP)communication network, an extensible messaging and presence protocol(XMPP) communication network, a cellular communications network, anysimilar communication networks, or any combination thereof for sendingand receiving data. The data communicated in the network 118 may includedata communicated via short messaging service (SMS), multimediamessaging service (MMS), hypertext transfer protocol (HTTP), direct dataconnection, wireless application protocol (WAP), e-mail, smart energyprofile (SEP), ECHONET Lite, OpenADR, or any other protocol that may beimplemented with the electronic device 102, the sensors 120 and 122, theuser device 124, the chronic disease database 126, the EHR database 128,a cloud server communication, and/or a gateway.

One or more of the sensors 120 and 122 may include any type of sensor togather sensor data related to a physical state of a patient. Forexample, one or more of the sensors 120 and 122 may include a globalpositioning system (GPS) sensor, an accelerometer sensor, a pedometersensor, a heart rate (HR) sensor, a blood pressure (BP) sensor, a bloodglucose sensor, an electromyography (EMG) sensor, an electrocardiogram(ECG) sensor, an electroencephalography EEG sensor, a Galvanic SkinResponse (GSR) sensor, a photoplethysmography (PPG) sensor, atemperature sensor, a sleep sensor, a posture sensor, a respirationsensor, a cardiac output sensor, a ballistocardiography (BCG) sensor, astress sensor, an emotion sensing system, or any other sensor to detectand/or gather data about a physical state of the patient. Alternativelyor additionally, one or more of the sensors 120 and 122 may include anytype of sensor to gather sensor data related to a mental state of thepatient. For example, one or more of the sensors 120 and 122 may detectemotional resilience, tiredness, mood, or any other mental state of thepatient. In some embodiments, one or more of the sensors 120 and 122 mayinclude on-body (e.g., wearable) devices and/or off-body (e.g.,non-wearable) devices.

The interpretation of sensor data may depend on a baseline and hence maybe adjusted based on different factors. For example, the interpretationof sensor data may be adjusted based on an age, a race, a gender, anethnicity, a health state, a health need, or any other appropriatepatient based factor that may affect the health of the patient.Additionally, the interpretation of sensor data may be adjusted based onwhen the sensor data is gathered such as for different months, days,seasons, or any other appropriate time based factor that may affect thehealth of the patient. The sensor data may permit quantification ofhealth habits in terms of activity, sleep, stress, posture, outdoortime, regularity of routine, number of cigarettes per day, number oftimes fast food is consumed, or number of times alcohol is consumed or arestaurant that serves alcohol is visited, what type of food is beingconsumed (e.g., amounts of salt, sugar, trans-fat, or alcohol) of thepatient. Alternatively or additionally, the sensor data may permitquantification of blood pressure, heart rate, heart rate variability,cardiac output, oxygen saturation, emotion markers, or pain markers ofthe patient. Furthermore, the sensor data may include data indicatinginteraction of the patient with a smartphone or similar device, whichmay permit quantification of a social life of the patient. The sensordata may be used to verify the various health scores discussed in thepresent disclosure, which may obviate exaggerated or understated healthconcerns of the patient.

In some embodiments, the sensor data may permit responses toquestionnaires to be obtained using the sensor data. The sensor data maybe used to generate patient profiles for various categories to which thepatient belongs. Additionally, the sensor data may be used to determinewhether the patient is deviating from standard norms of the patient.

The chronic disease database 126 may include any computer-based sourcefor collecting and/or storing chronic data related to patients that haveone or more chronic diseases (e.g., arthritis, diabetes, epilepsy, heartdisease, chronic obstructive pulmonary disease (COPD), asthma, cancer,cardiovascular disease, or any other chronic disease). In someembodiments, the chronic disease database 126 may include a sufficientlylarge and publicly available database (such as CDC database and/or anNHIS database). The chronic data may be broken down into statisticaldata sets based on regions, states, counties, and/or cities of patientsincluded in the chronic data. For example, the chronic data related topatients that have diabetes may be broken down into data sets thatinclude all patients located in an entire region such as the entirePacific Northwest, patients located in all of a single state such asWashington, patients located only in one county of the state such asKing County, or patients located just in one city, town, or metropolitanarea such as Seattle.

The chronic data may include psychophysiological, physiological,healthcare choices, lifestyle choices, and/or social profiles of thedifferent patients included in the chronic data. Likewise, the chronicdata may include patient responses to various health relatedquestionnaires, such as a HRQOL, HALex, patient healthcare questionnaire2 (PHQ2), or any other health related questionnaires. The patientresponses then may be stored as patient HRQOL input, patient HALexinput, and/or patient PHQ2 input. The chronic data may also includesensor data, such as the sensor data gathered by the sensors 120 and122.

The patient HRQOL input may include a perceived overall quality of lifeof the patient by determining a state of physical and/or mental healthof individual patients or a group of patients. The patient HRQOL inputmay also include VAS based quantification of broad multidimensionalsubjective evaluations of both positive and negative aspects of the lifeof patients. The patient HRQOL input may be related to chronic diseasesand/or risk factors of the patients. For example, the patient HRQOLinput may include a list of chronic diseases each patient has beendiagnosed with along with any risk factors that may affect the chronicdiseases of the patients. Risk factors may include body mass index(BMI), physical inactivity, smoking, alcohol and/or other risk factors.

The data regarding functional limitation (patient activity limitationdata such as HALex input) may indicate limitation of activity of thepatients included in the chronic data. For example, the patient HALexinput may indicate whether the patients are receiving help with dailytasks such as grooming, getting dressed, eating, or other activitiesthough previously these were being done unassisted. The patient HALexinput may also include a database which may have data for a similargroup of patients across a wide range of regions. For example, thepatient HALex input may be collected and analyzed by city, state, and/orregion that the different patients reside within.

The patient PHQ2 input may indicate PHQ2 depression score of thepatients along with in the context of the patient's depressionespecially the historic pattern. For example, the patient PHQ2 input mayindicate whether the patients are experiencing worsening depression ornot. The patient PHQ2 input may be adjusted based on factors that mayaffect depression. For example, the patient PHQ2 input may be adjustedbased on a season, time of day, recent life experiences, or other data.

The EHR database 128 may include any computer-based source for patientdata (e.g., EHR data) related to patients that visit an office of aphysician associated with the EHR database 128. The EHR data may includeinformation gathered during examination by physicians or staff at theoffice of the physician. The EHR data may include patient data thatindicates at least one of a biological profile of the patient, apsychological profile of the patient, a social profile of the patient,physician notes related to the biological profile of the patient,physician notes related to the psychological profile of the patient, andphysician notes related to the social profile of the patient.Additionally, the EHR data may include data related to clinical symptomssuch as COPD of the patients; risk factors such as BMI and BP of thepatients; extenuating factors such as pain of the patients; andpsycho-social factors such as financial status of the patients. The EHRdata may be combined with the sensor data to determine responses toquestionnaires without the patient providing additional information.Using the sensor data combined with the EHR data to determine responsesto the questionnaires without the patient providing additionalinformation may reduce overhead associated with providing thequestionnaires and/or storing information related to the questionnairesand may also improve the patient's experience by reducing and/oreliminating the patient's attentive involvement in responding to thequestionnaires, e.g., the patient may be involved by going about thepatient's regular activities while the sensor(s) collect data withoutotherwise having to take time to attentively respond to questionnaires.

The user device 124 may include a computer-based hardware device thatincludes a processor and communication capabilities. The user device 124may be coupled to the network 118 to communicate data with any of theother components of the system 100. For example, the user device 124 maycommunicate with the electronic device 102 to provide data or receivedata related to the patient. Some examples of the user device 124 mayinclude a mobile phone, a smartphone, a tablet computer, a laptopcomputer, a desktop computer, a set-top box, a virtual-reality device, aconnected device, or other user device. The user device 124 may includea processor-based computing device. For example, the user device 124 mayinclude a hardware server or another processor-based computing deviceconfigured to function as a server.

The user device 124 may also include an interface for facilitatingcommunication with the patient. For example, the interface may beconfigured to communicate with external devices such as input devices(e.g., keyboard, mouse, pen, voice input device, touch input device,and/or others), or the sensors 120 and 122. Alternatively oradditionally, the user device 124 may include an integrated input deviceto receive input from the patient, such as a touchscreen display, avirtual keyboard, or other input device.

The electronic device 102 may include a computer-based hardware devicethat includes a processor and communication capabilities. The electronicdevice 102 may be coupled to the network 118 to communicate data withany of the other components of the system 100. Some examples of theelectronic device 102 may include a mobile phone, a smartphone, a tabletcomputer, a laptop computer, a desktop computer, a set-top box, avirtual-reality device, a connected device, or other electronic device.The electronic device 102 may include a processor-based computingdevice. For example, the electronic device 102 may include a hardwareserver or another processor-based computing device configured tofunction as a server. The electronic device 102 may include aquestionnaire module 104, a disease calculator 106, a chronic burdenmodule 108, a graded escalation module 110, an under diagnosis module112, a lifestyle choice module 114, a total health module 116, and amemory 117. Although not depicted in FIG. 1, the electronic device 102may additionally include one or more processors and/or communicationinterfaces.

The memory 117 may include computer-readable storage media forcollecting or storing data thereon. For example, the memory 117 mayinclude computer-readable storage media that may be tangible ornon-transitory computer-readable storage media such as Random AccessMemory (RAM), Read-Only Memory (ROM), Electrically Erasable ProgrammableRead-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) orother optical disk storage, magnetic disk storage or other magneticstorage devices, flash memory devices (e.g., solid state memorydevices), or any other tangible and non-transitory storage medium whichmay be used to store data that may be accessed by a general-purpose orspecial-purpose computer.

The memory 117 may store various data in any data structure, such as arelational database structure. For example, the memory 117 may includecollected data obtained from one or more of the sensors 120 and 122, theuser device 124, the chronic disease database 126, and/or the EHRdatabase 128.

The questionnaire module 104 may include software executable by or onthe electronic device 102. For example, the questionnaire module 104 mayinclude code stored on the electronic device 102 that may be executedline-by-line by the processor of the electronic device 102 and/or may beloaded into the memory 117 and executed by the processor of theelectronic device 102 to perform or control performance of one or moreoperations described herein in connection with the questionnaire module104. Alternatively or additionally, the questionnaire module 104 may beimplemented in hardware, e.g., as an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA), or otherhardware device configured to perform or control performance of one ormore operations described herein in connection with the questionnairemodule 104.

The questionnaire module 104 may be configured to provide one or morehealth related questionnaires to the patient via user device 124. Forexample, the questionnaire module 104 may provide the HALex, PHQ2, ahealthy lifestyle and personal control questionnaire (HLCPQ), HRQOL,and/or any other appropriate health based questionnaires. Alternativelyor additionally, the questionnaire module 104 may provide a euroQOl fivedimension (EQ-5D-3L), a health utilities index mark 2 (HUI2), a healthutilities index mark 3 (HUI3), a short form six dimension (SF-6D),and/or quality of well-being scale questionnaires, and/or other suitablequestionnaires. The identification, selection, and/or timing of theadministration/provision of the questionnaire(s) to the patient via userdevice 124 may be controlled by the chronic burden module 108, thegraded escalation module 110, the lifestyle choice module 114, and/orthe total health module 116.

In some embodiments, the questionnaire to assess the functional activitylimitation, such as HALex questionnaire, may include one or morequestions directed to a functional status or limitation of activity ofthe patient. Questions in the HALex questionnaire may include: “Becauseof a physical, mental, or emotional problem do you need the help ofother persons with your personal care needs, such as eating, bathing,dressing, or getting around inside the home?” “Because of a physical,mental, or emotional problem do you need the help of other persons inhandling routine needs, such as everyday household chores, doingnecessary business, shopping, or getting around for other purposes?”“Does a physical, mental, or emotional problem now keep you from workingat a job or business?” “Are you limited in the kind or amount of workyou do because of physical, mental, or emotional problems?” and “Are youlimited in any way in any activities because of the physical, mental, oremotional problems?”, and/or other suitable questions about a functionalstatus or limitation of activity of the patient.

In some embodiments, the HRQOL questionnaire may include one or morequestions directed to general health factors that encompass one or bothof physical and mental health of the patient. Questions in the HRQOLquestionnaire may include: “Would you say that in general your health isexcellent, very good, good, fair, or poor?” “Now thinking about yourphysical health, which includes physical illness and injury, for howmany days during the past thirty days was your physical health notgood?” “Now thinking about your mental health, which includes stress,depression, and problems with emotions, for how many days during thepast thirty days was your mental health not good?” and “During the pastthirty days, for about how many days did poor physical or mental healthkeep you from doing your usual activities, such as self-care, work, orrecreation?”, and/or other suitable questions about general healthfactors that may encompass one or both of physical and mental health ofthe patient.

In some embodiments, the HLCPQ questionnaire may include one or morequestions directed to lifestyle choices of the patient. The HLCPQquestionnaire may include one or more subjects related to lifestylechoices of the patient. For example, the one or more subjects mayinclude dietary health choices, dietary harm avoidance, daily routine,organized physical exercise, social and mental balance, and/or risktaking choices of the patient. Questions in the dietary health choicesof the patient may include: ““Do you carefully control the amount offood on your plate at mealtime?”?” “Do you check the food labels beforebuying a product?” “Do you calculate the calories of your meals?” “Doyou limit fat in your meals?” “Do you limit meat in your diet?” and “Doyou feel you eat sufficient amount of fruits and vegetables?”, and/orother suitable questions about dietary health lifestyle choices of thepatient.

In some embodiments, questions in the dietary harm avoidance of thepatient may include “Do you eat pre-packed, frozen or fast food?” “Doyou avoid soft drinks?” ““Do you change your eating habits when stressedor disappointed?” “Do you overeat when eating out with friends?”, “In atypical week, do you eat most of your [all your] meals at around thesame time?”. “Are you careful about not missing a meal each day?”, “Doyou eat a nutritionally balanced breakfast?”, and/or other suitablequestions about dietary harm avoidance lifestyle choices of the patient.Questions in the daily routine of the patient may include: “Do you havea regular schedule you follow every day?” “Do you sleep at around thesame time each day?” “Do you eat breakfast at the same time each day?”“Do you eat lunch at the same time each day?” and “Do you eat dinner atthe same time each day?”, and/or other suitable questions about dailyroutine lifestyle choices of the patient.

In some embodiments, questions in the organized physical exercise of thepatient may include: “Do you undertake moderate or rigorous physicalactivities for at least one hundred minutes per week?” and “Do youexercise in an organized/regular manner?”, and/or other suitablequestions about organized physical exercise lifestyle choices of thepatient. Questions in the social and mental balance of the patient mayinclude: “Do you use any support system of friends and/or family whenyou face a personal problem or worry?” “When going through difficulties,do you try to remain optimistic or concentrate on positive thoughts?”“Do you consciously try to relax before sleeping?” “Do you care aboutmeeting and discussing with your family on a daily basis?” and “Do youbalance your time between work, personal life, and leisure?”, and/orother suitable questions about social and mental balance lifestylechoices of the patient.

In some embodiments, questions in the risk-taking choices of the patientmay be treated differently. The patient's response for the questions inthe risk taking questionnaire may be used to scale his score obtainedfrom the rest of the HLPCQ survey. The risk taking questionnaire mayinclude: “Do you smoke? If Yes, then how many cigarettes a day?” “Do youconsume alcohol? If so, how many servings a day?” “Do you get exposed tosignificant pollution besides during commute? If so then for how manyminutes per day?” “Do you have a congested commute by road? If so thenfor how many minutes per day?” “Do you use sunscreen or take measures toprotect yourself from direct (afternoon) sun?” and “Do you ever skipmedications? If so, how many times a week?”, and/or other suitablequestions about risk taking lifestyle choices of the patient.

The HLPCQ questions can be used to compute behavioral score of thepatient, dubbed as BICO score (Behavior Index Comprehensive ScOre).

The questions may be categorized into 6 categories namely C1, C2, C3,C4, C5 and C6. A particular question can belong to only one category butone category can have multiple question. The answer to each question maybe given one of the following weights 0, 1.25, 2, 5, 3.75 or 5. Theanswer to each may be stored in variable A_(ij), where i is one of the 6categories and j is a particular question in the category i. When allthe questions are answered, a score may be calculated as follows:C_(i)=Σ(A_(ij)) where i=1 to 6 representing each category and j=1 to nwith n being the number of questions in that category. Once the scorefor each category is calculated the behavior score, BICO, may becalculated by appropriately combining the scored categories. Forexample, one method of combining the categories adds all category scoresexcept the risk taking category, and multiplies the resulting value withappropriately weighted risk taking factor. One embodiment of such amethod is the following formula:BICO=(C1+C2+C3+C4+C5)*(1−C6/20+R)*100/88. Here R is a regularizationfactor that allows the risk factor to appropriately scale the compositevalue obtained from all other categories. In the formula describedabove, R is given a value of 0.1.

In some embodiments, the questions that lead to computation of lifestylechoices may be determined using input from the sensors. For example, onecan use sensors that can analyze the quantity and the quality of thepatient's diet. For example, sensors that use accelerometer in a smartglass or anywhere in an upper torso of the patient that can detect thechewing of the user help analyze the dietary dimensions. Sensors whichwork by analyzing the image of food and quantity of food left after themeal are additional such approaches. One can also use sensors thatanalyze whether a person is smoke free and the frequency of possiblesmoking as is already known in the published state of the art. One canalso use sensors including GPS that analyze the extent of exposure topollution. One can also use sensors that determine the regularity of theschedule which simply look at the type of activities, time and durationof sleep, location as function of time and calendar, time and extent ofeating, to determine the regularity of the lifestyle. One can alsocombine the above lifestyle choice calculating modules with stress andemotion detection to quantify the patient's lifestyle choices. One canalso combine the above lifestyle choice calculating modules with speechrecognition to further quantify the patient's lifestyle choices. One canalso combine a breath analyzer with the above sensing modalities tofurther quantify the extent of alcohol a patient drinks.

In some embodiment, the patient response is auto-filled via the sensorinput as described above and then validated by the patient. In someembodiment, the sensor derived input(s) can over-ride the patientprovided input(s). In some embodiment, the patient response is taken asthe weighted average of sensor derived input and the patient derivedinput. In some other embodiment, the patient credibility is given aweightage by the care-provider which is used to create the compositepatient response by combining patient provided input and input derivedvia sensor. In some embodiment, the physician is provided patient sensorvalue that can be compared to patient's response to determine theveracity of the patient's response, and hence create the patientcredibility. In some embodiment, the physician is provided a range ofcredibility weightage to provide to the patient-derived input so that arange of scores are computed for the patient and based on thephysician's clinical assessment he can empirically determine thecredibility weightage.

In some embodiment, for each response the patient provides, he is askeda set of questions specific to that response to validate the patientprovided inputs and guide him to make the correct assessment. Forexample, suppose a patient is asked if he needs help to do the dailytasks of living. Now, if his response is YES but it is due to a mistakenunderstanding of what it means to get help to get dressed then theadditional questions serve to clarify his input.

In some embodiment, the sensor derived input is calibrated by astatistical analysis of the expected value from a matching set of users.In some embodiment, the sensor derived input is calibrated by astatistical analysis of the expected value from a matching set of usersas determined by the physician with or without an analysis of thestatistical information.

In some embodiments, the questionnaire module 104 may include a generalstate of health of the patient question, which may include “Would yousay that in general your health is excellent, very good, good, fair, orpoor?”

The user device 124 may display the questionnaires to the patient, oneat a time or two or more simultaneously. The patient may provideresponses (e.g., patient input) to the questionnaires via the one ormore input devices coupled to the user device 124. The user device 124may provide the patient input to the electronic device 102 via thenetwork 118. The patient input may be stored in the memory 117 forfurther manipulation or later access by the electronic device 102.

The disease calculator 106 may include software executable by or on theelectronic device 102. For example, the disease calculator 106 mayinclude code stored on the electronic device 102 that may be executedline-by-line by the processor of the electronic device 102 and/or may beloaded into the memory 117 and executed by the processor of theelectronic device 102 to perform or control performance of one or moreoperations described herein in connection with the disease calculator106. Alternatively or additionally, the disease calculator 106 may beimplemented in hardware, e.g., as an ASIC, an FPGA, or other hardwaredevice configured to perform or control performance of one or moreoperations described herein in connection with the disease calculator106.

The disease calculator 106 and/or the questionnaire module 104 may beconfigured to provide one or more disease calculator questionnairesabout one or more chronic diseases to the patient via the user device124. For example, the disease calculator 106 and/or the questionnairemodule 104 may provide one or more disease calculator questionnaires. Anexample, is the questionnaires available athttps://www.adma.org.au/clearinghouse.html at least as of Dec. 12, 2017and/or the disease calculator 106 may be implemented as any of thecalculators available at the same source. As another example, thedisease calculator 106 and/or the questionnaire module 104 may provideone or more disease calculator questionnaires available athttps://siteman.wustl.edu/prevention/ydr/ orhttps://reference.medscape.com/guide/medical-calculators at least as ofMar. 28, 2018 and/or the disease calculator 106 may be implemented asany of the calculator available at the same source. Alternatively oradditionally, the disease calculator 106 may determine a chronic diseasecalculator score based on patient responses to the one or morequestionnaires.

The user device 124 may display the disease calculator questionnaires tothe patient. The patient may provide responses (e.g., disease calculatorpatient input) to the disease calculator questionnaires via one or moreinput devices coupled to the user device 124. The user device 124 mayprovide the disease calculator patient input to the electronic device102 via the network 118. The disease calculator patient input may bestored in the memory 117 for further manipulation or later access by theelectronic device 102. The identification, selection, and/or timing ofthe administration/provision of disease calculator questionnaires to thepatient via the user device 124 may be controlled by the chronic burdenmodule 108, the graded escalation module 110, the lifestyle choicemodule 114, and/or the total health module 116.

The graded escalation module 110 may include software executable by oron the electronic device 102. For example, the graded escalation module110 may include code stored on the electronic device 102 that may beexecuted line-by-line by the processor of the electronic device 102and/or may be loaded into the memory 117 and executed by the processorof the electronic device 102 to perform or control performance of one ormore operations described herein in connection with the gradedescalation module 110. Alternatively or additionally, the gradedescalation module 110 may be implemented in hardware, e.g., as an ASIC,an FPGA, or other hardware device configured to perform or controlperformance of one or more operations described herein in connectionwith the graded escalation module 110.

The graded escalation module 110 may be configured to determineshort-term triage of the patient based on factors that may includeresponses by the patient to one or more questionnaires. Similarly, thegraded escalation module 110 may be configured to predict a likelihoodof the patient experiencing an acute event (e.g., visiting an emergencyroom (ER) and/or hospitalization) in the near future based on changes inshort-term health aspects of the patient. The likelihood of the patientexperiencing an acute event in the near future may be determined basedon the patient input (e.g., the patient responses to the questionnairesprovided by the questionnaire module 104 and/or the disease calculatorquestionnaires provided by the disease calculator 106). Alternatively oradditionally, the likelihood of the patient experiencing an acute eventin the near future may be determined based on the chronic data includedin the chronic disease database 126 and/or the EHR data included in theEHR database 128.

For example, the likelihood of the patient experiencing an acute eventin the near future may be determined based on a number of unhealthy daysof the patient and/or an amount of pain the patient is currentlyexperiencing. As another example, the likelihood of the patientexperiencing an acute event in the near future may be determined basedon the number of unhealthy days of the patient, patient functionalactivity limitation input, such as HALex input, and/or the amount ofpain the patient is currently experiencing which can be input using aVAS (visual analog scale) that is calibrated, for example, between 0 to10, or between 0 to 100. As yet another example, the likelihood of thepatient experiencing an acute event in the near future may be determinedbased on the number of unhealthy days of the patient, the patient HALexinput, the amount of pain the patient is currently experiencing, and/orpatient PHQ2 input. Alternatively, the likelihood of the patientexperiencing an acute event in the near future may be determined basedon the patient HALex input and/or patient general health input.

The graded escalation module 110 may determine the likelihood of thepatient experiencing an acute event in the near future without thepatient having to visit an office of the physician.

In some embodiments, the graded escalation module 110 may provide ananatomically detailed human avatar (referred to herein as ‘avatar’) tothe patient via the user device 124. The avatar may allow the patient toenter the amount of pain the patient is experiencing, where that patientis experiencing pain, and/or whether the pain is musculoskeletal orvisceral organ by using a visual analog scale (VAS) pain scale. Theamount of pain the patient is experiencing, where that patient isexperiencing pain, and/or whether the pain is musculoskeletal orvisceral organ may be stored as a VAS score. The VAS score may bereceived from the user device 124 by the electronic device 102 andstored in the memory 117. Alternatively or additionally, the VAS scoremay be received from the user device 124 by the graded escalation module110. The graded escalation module 110 may be configured to determine anetiology of pain of the patient using the avatar.

In some embodiments, the graded escalation module 110 may direct thequestionnaire module 104 to provide one or more questions to the patientvia the user device 124 in response to the patient indicating that theyare experiencing pain or discomfort in one or more regions of the body.For example, the questionnaire may include a somatic symptomquestionnaire. The questions may be directed to what type of pain ordiscomfort the patient is experiencing and its intensity or any otherappropriate question related to the pain or discomfort of the patientwith respect to the region of the body under question. A patientresponse to the one or more questions along with a current VAS score maybe collected and stored as VAS data (e.g., a two-dimensional VAS pain ordiscomfort scale)

In some embodiments, the VAS score may be judged relative to a previousscore or a standard score. This can happen if the patient has previouslybeen diagnosed with a specific chronic disease for which an absolutescale of maximum discomfort or abnormality is known or the VAS scalefrom a previous episode is known. In such cases, a simple normalizationmay be applied to the VAS score with respect to the maximum score or thescore during the previous instance. For example, the VAS scale foramnesia may be adjusted up/down to provide a normalized VAS score forthe severity of amnesia. Additionally, the VAS scale for a new episodeof disabling back pain may be scored with respect to the previousdisabling episode. In suitable cases, such as when patient is in amalignant state where no recovery is possible and only palliativetreatment can be provided, then the patient's VAS score can be comparedto the scores of the matching patient

The graded escalation module 110 may repeat the acquisition of the VASscore and/or the VAS data after a specified period of time has elapsedsince a VAS score and/or VAS data was last obtained from the patient.The specified period of time may be equal to or greater than two weeks.In some embodiments, the specified period of time may be less than twoweeks. The graded escalation module 110 may notify the patient that theyare supposed to indicate on the avatar and/or respond to the one or morequestions after the specified period of time has elapsed. Each time aVAS score and/or VAS data is received, the VAS score and/or VAS data maybe stored in the memory 117 and/or the graded escalation module 110 as acurrent VAS score and/or a current VAS data.

In some embodiments, the graded escalation module 110 may generate afirst quality of health marker based on a comparison of the current VASscore and/or the current VAS data to a previous VAS score and/or aprevious VAS data stored in the memory 117 and/or the graded escalationmodule 110. In some embodiments, the first quality of health marker mayindicate a first dimension of the health of the patient as a paindimension of the health of the patient. In these and other embodiments,the first quality of health marker may indicate whether additionalexamination of the health of the patient is to be performed by thephysician.

In some embodiments, the graded escalation module 110 may determinewhether the VAS score exceeds a VAS threshold value (such as around 8 inVAS pain scale). Additionally or alternatively it may determine thedifference between the current VAS score and/or the current VAS data andthe previous VAS score and/or the previous VAS data exceeds a thresholdvalue. If a flag is raised because the VAS score does not exceed athreshold value or the difference between the current VAS score and/orthe current VAS data and the previous VAS score and/or the previous VASdata does not exceed the threshold value, additional examination of thepatient may not be performed by the graded escalation module 110 and thegraded escalation module 110 may wait until the specified period of timehas elapsed before repeating the acquisition of a subsequent VAS scoreand/or VAS data. Alternatively, if the VAS score exceeds a VAS thresholdvalue or the difference between the current VAS score and/or the currentVAS data and the previous VAS score and/or the previous VAS data exceedsthe threshold value, additional examination of the patient may beperformed by the graded escalation module 110.

In some embodiments, the graded escalation module 110 may determinewhether the first quality of health marker indicates that the VAS scoreof somatic discomfort or pain is in the upper half of a range of VASscores. If the first quality of health marker indicates that the VASscore is in the upper half of the range of VAS scores, the gradedescalation module 110 may generate and provide a suitable alert to thephysician or care-provider. The alert may include a quality of healthresult that indicates that the VAS score is in the upper half of therange of VAS scores and that the patient has an increased likelihood toexperience an acute event in the near future and thus should have anadditional in person examination performed by the physician and/orcare-provider as soon as possible. In VAS scale of pain, a score ofabove 7.5 may show fairly severe pain and above 8.5 may be a cause forprompt attention. In the same way, in VAS health score, a score below 4may show poor health that may require prompt attention.

In some embodiments, the graded escalation module 110 may direct thequestionnaire module 104 to provide the general health questionnaire tothe patient via the user device 124. In these and other embodiments, thefirst quality of health marker may be based on the patient generalhealth input. If the patient general health input indicates that, ingeneral, the health of the patient is excellent, very good, or good,additional examination of the patient may not be performed by the gradedescalation module 110 and the graded escalation module 110 may waituntil the specified period of time has elapsed before repeating theacquisition of patient general health input. Alternatively, if thepatient general health input indicates that, in general, the health ofthe patient is fair or poor, additional examination of the patient maybe performed by the graded escalation module 110. In some embodiments,if the health of the patient is being self-reported as fair or poor thento confirm the health status, the patient may be asked to provide thesame information using an equivalent scale which is differently numberedsuch as a VAS scale where a score of 2 or less represents poor healthand a score between 2 to 4 represents fair health. If the self-reportedscores from both mechanisms do not match then the patient may beprompted again to provide the general status of his health till both thescores converge. In some embodiments, the first quality of health markermay include the first dimension as a general state of health dimensionof the patient. In some embodiments, the first quality of health markermay be based on at least two of the patient general health input, theVAS score, and/or the VAS data. In some embodiments, if the health thefirst quality of health marker may additionally or alternatively bebased on sensor data collected by the sensors 120 and 122. For example,the first quality of health marker may be based on sensor data collectedby a PPG sensor, an ECG sensor, an EMG sensor, an accelerometer, a BPsensor, a blood glucose sensor, a respiration sensor, a posture sensor,a temperature sensor, an oxygen-saturation sensor, a cardiac outputsensor, a sleep sensor, a stress sensor, an emotion sensing system, etc.

The graded escalation module 110 may compare the first quality of healthmarker to a first quality of health marker baseline value. If adifference between the first quality of health marker and the firstquality of health marker baseline value exceeds a first quality ofhealth marker threshold value, the graded escalation module 110 maygenerate and provide an alert to the physician or care-provider. Thealert may include the quality of health result that indicates that thedifference between the first quality of health marker and the firstquality of health marker baseline value exceeds the first quality ofhealth marker threshold value and that the patient is likely toexperience an acute event in the near future and should have additionalin person examination performed by the physician and/or care-provider assoon as possible.

The graded escalation module 110 may adjust one or more of a number ofthe questionnaires, a number of questions that are included in thequestionnaires, and a type of questionnaires that are provided to thepatient via the user device 124 based on the first quality of healthmarker. For example, if the difference between the current VAS scoreand/or the current VAS data and the previous VAS score and/or previousVAS data is between ten percent and twenty percent, the gradedescalation module 110 may instruct the questionnaire module 104 toprovide a single questionnaire with a reduced number of questions to thepatient via the user device 124. As another example, if the differencebetween the current VAS score and/or the current VAS data and theprevious VAS score and/or previous VAS data is between twenty percentand thirty percent, the graded escalation module 110 may instruct thequestionnaire module 104 to provide a single questionnaire with anincreased number of questions to the patient via the user device 124. Asyet another example, if the patient general health input indicates apoor general state of health of the patient, the graded escalationmodule 110 may instruct the questionnaire module 104 to provide a singlequestionnaire with an increased number of questions to the patient viathe user device 124.

Adjusting the number of questionnaires, the number of questions that areincluded in the questionnaires, and/or the type of questionnaires thatare provided to the patient, may allow more relevant data to becollected by the graded escalation module 110, as warranted, whilecollecting less data patient data when not warranted. For example, if apatient is relatively healthy and is not experiencing much pain,questions related to high amounts of pain may not be relevant and may beomitted from the collection of data. Such adjustments may improve thefunctioning of the electronic device 102, the user device 124, and/orthe network 118 compared to always administering all questionnairesand/or questions to a patient by administering only a subset of thequestionnaires and/or questions as warranted. By administering onlythose questionnaires and/or questions that may be warranted, a reductionin communication bandwidth, processor bandwidth, and/or storagerequirements may be achieved in one or more of the electronic device102, the network 118, and/or the user device 124.

The questionnaires that the graded escalation module 110 instructs thequestionnaire module 104 to provide to the patient via the user device124 may provide an acute assessment of issues related to the quality ofhealth of the patient. For example, the questionnaires may be related tomobility of the patient (e.g., the HALex questionnaire) or the qualityof life of the patient (e.g., the HRQOL questionnaire). The gradedescalation module 110 may generate a second quality of health marker ofthe patient based on the sensor provided HALex input (e.g., a presentactivity indicator) and/or the patient HRQOL input received in responseto the corresponding questionnaire. The second quality of health markermay provide additional quantification of the quality of health of thepatient in addition to the first quality of health marker.

In some embodiments, the graded escalation module 110 may instruct thequestionnaire module 104 to provide the HALex questionnaire to thepatient via the user device 124. Scores associated with responses toquestions included in the patient HALex input may range between 0.1 to1.0. The patient HALex input may indicate a perception of the patient oftheir general state of health (e.g., a perception score). Alternativelyor additionally, the patient HALex input may indicate a functionalstatus (e.g., a functional score) of the patient.

The graded escalation module 110 may determine an overall HALex scorebased on one or both of the perception score and the functional score.In some embodiments, if the perception score is high but the functionalscore is low, the overall HALex score may be low. In some embodiments,if the perception score is low but the functional score is high, theoverall HALex score may be high.

In some embodiments, the graded escalation module 110 may scale theperception score based on chronic data obtained from the chronic diseasedatabase 126. For example, the graded escalation module 110 may accessresponses from other patients (e.g., NHIS data). The chronic data mayinclude an averaged perception score of a variety of categories ofpatients. For example, the perception scores may be averaged accordingto various categories or combination thereof which influences the healthof a patient even when all other things are equal such as age, gender,race, BMI, chronic disease, alcohol addiction, smoking addiction, dailyactivity level, availability of health insurance, education level,income level, place of residence, etc. For example, the gradedescalation module 110 may multiply each perceived health score by acoefficient determined based on one or more such categories of which thepatient is a member and which also have a markedly higher or lower riskthan the mean value for the baseline categories.

The second quality of health marker may additionally or alternatively bebased on sensor data collected by the sensors 120 and 122. For example,the first sensor 120 and/or the second sensor 122 may include a motionsensor (e.g., accelerometer) configured to determine how much thepatient moves during a period of time. As another example, the firstsensor 120 and/or the second sensor 122 may include a smart phoneapplication configured to record daily events (e.g., number of baths,eating, getting dressed, or other events) of the patient and if anotherperson assisted the patient. As yet another example, the first sensor120 and/or the second sensor 122 may include a GPS sensor configured todetermine whether another known GPS sensor was detected within a certainproximity of the patient during daily events of the patient.

The graded escalation module 110 may compare the second quality ofhealth marker to a second quality of health marker baseline value. If adifference between the second quality of health marker and the secondquality of health marker baseline value exceeds a second quality ofhealth marker threshold value, the graded escalation module 110 maygenerate and provide the alert to the physician and/or care-provider.The alert may include the quality of health result that indicates thatthe difference between the second quality of health marker and thesecond quality of health marker baseline value exceeds the secondquality of health marker threshold value and that the patient is likelyto experience an acute event in the near future and should haveadditional in person examination performed by the physician and/orcare-provider as soon as possible.

Alternatively, if the difference between the second quality of healthmarker and the second quality of health marker baseline value does notexceed the second quality of health marker threshold value, the gradedescalation module 110 may instruct the questionnaire module 104 toprovide the HRQOL questionnaire to the patient via the user device 124.The patient HRQOL input received in response to the HRQOL questionnairemay indicate a total number of physical unhealthy days and/or mentalunhealthy days of the patient. Likewise, the patient HRQOL input mayinclude a non-ordinal unhealthy days score (e.g., cardinal healthscore). In some embodiments, the non-ordinal unhealthy days score may bedetermined using factor analysis theory in statistics where based upon avariability among the observed variables, a smaller number of unobserved(e.g., latent) variables may be used to explain the observed variables.Additionally, the observed variables may include linear combinations ofthe unobserved variables and an error terms. The factor analysis theorymay be performed using well known methods in the field.

In some embodiments, the graded escalation module 110 may generate thequality of health result based on at least one of the difference betweenthe first quality of health marker and the first quality of healthmarker baseline value and the second quality of health marker and thesecond quality of health marker baseline value. The graded escalationmodule 110 may generate and provide the alert that includes the qualityof health result to the physician and/or care-provider. The quality ofhealth result may indicate that the patient is not likely to experiencean acute event in the near future and can have additional in personexamination performed by the physician and/or care-provider at a laterdate.

The patient HRQOL input may indicate a current level, mood, andcorrelated data such as health risks and conditions; functional status;social support; and/or socioeconomic status of the patient. The patientHRQOL input may provide a way to determine an impact of the health ofthe patient on the quality of life of the patient outside of what can bedetermined by detailed medical analysis (e.g., use of a microscope). Thepatient HRQOL input may be used to determine a burden of preventabledisease, injuries, and disabilities of the patient. Additionally, thenumber of physical healthy days and/or mental healthy days may be usedto determine outcomes of the health of the patient and a predictor ofchanges in the health of the patient.

Alternatively or additionally, the patient HRQOL input may containrelational model that provide insight into relationships between dataincluded in the patient HRQOL input and one or more risk factors.Furthermore, the patient HRQOL input may provide additional ways forphysicians to monitor progress of the patient so as to achieve mostMedicare health objectives whether stated or unstated. Also, the patientHRQOL input may be used as valid indicators of unmet health needs of thepatient and to predict intervention outcomes. The patient HRQOL inputmay indicate practices that affect the physical and/or mental health ofthe patient and/or the patients that are included in the chronic data.

In some embodiments, the patient HRQOL input may provide new insightsinto the relationship between HRQOL and clinically-measured healthcharacteristics and conditions such as BP; physical strength andendurance; oral health; and mental health etc. For example, if a patientwith pre-chronic conditions is being considered then a certain quantumof change to the pre-chronic conditions may occur before the chroniccondition sets in. For example, in older adults the HALEX score in ascale of zero to one hundred may change by fourteen points when thepatient has developed heart failure. Similarly, a deterioration of HALEXscore or any other health score by three percent may indicate worseningpre-chronic conditions such as thyroid but may not be indicative of achronic condition like heart failure.

The HRQOL data of a patient may be interpreted in terms of what isexpected and what is observed. The graded escalation module 110 mayadjust the patient HRQOL input based on one or more factors that mayaffect perceived physical and/or mental unhealthy days by the patient.Additionally or alternatively, the graded escalation module 110 mayadjust the patient HRQOL input based on the chronic data included in thechronic disease database 126. In some embodiments, the patient HRQOLinput may be adjusted based on demographic, socioeconomic, phenotype,genotype, and/or health pattern factors of the patient. For example, thepatient HRQOL input may be adjusted if the patient lives in a regionthat experiences a higher number of days of cloudy weather. As anotherexample, the patient HRQOL input may be adjusted by using a weightedHRQOL score based on an overall HRQOL status of patients included in thechronic data. The HRQOL data of a patient may be interpreted in terms ofwhat is expected and what is observed. Needless to say, the modules canbe configured so that the HRQOL data of a patient reflects thedifference between two perceived health markers; one may represent theexpectation of the physician for the patient, and second that isactually being observed by patient as the status of his health.

The graded escalation module 110 may compare the patient HRQOL input tothe objective markers included in the chronic database. For example, thechronic data may include the national health and nutrition examinationsurvey (NHANES) data. The NHANES data may include objective measures ofphysical health and blood tests for a group of patients. The NHANES datamay include the number of reported unhealthy days and activitylimitation days of the group of patients in relation to risk factorssuch as measured BMI and/or physical endurance, as well as to reportednutritional and physical activity patterns of the group of patients.

In some embodiments, the HRQOL questionnaire may be provided as part ofan overall behavioral risk factor surveillance system (BRFSS) healthdetermination. The BRFSS health determination may be based on at leasttwo of the VAS score, the VAS data, and the patient HRQOL input. TheBRFSS health determination may provide an ordinal number to provide atrend analysis for the physician. In some embodiments, the BRFSS healthdetermination may be further based on the sensor data obtained from thesensors 120 and 122. In some embodiments, as the system startsincorporating a larger sample of the patient wired with sensors, one canuse the metadata derived from the sensor data as the baseline. Themetadata may include the mood, activity, lifestyle trend, sleep, stress,or any such psychophysiological components of health that can be deducedfrom the sensor data.

The graded escalation module 110 may compare the total number ofphysical unhealthy days to a physical unhealthy days threshold value.Likewise, the graded escalation module 110 may compare the total numberof mental unhealthy days to a mental unhealthy days threshold value. Ifeither of the total number of physical unhealthy days or the totalnumber of mental unhealthy days exceeds the corresponding thresholdvalue, the graded escalation module 110 may generate and provide thealert to the physician and/or care-provider. The alert may indicate thateither the total number of physical unhealthy days or the total numberof mental unhealthy days exceeds the corresponding threshold value interms of the actual or in terms of the deviation seen from thestatistical viewpoint, and the probability computed from the data ofsimilar patients that the patient is likely to experience an acute eventin the near future and should have additional in person examinationperformed by the physician and/or care-provider as soon as possible. Thequality of health result may also include the difference between thetotal number of physical unhealthy days and the physical unhealthy daysthreshold value.

Additionally or alternatively, if the total number of mental unhealthydays exceeds the mental unhealthy days threshold value, the gradedescalation module 110 may direct the questionnaire module 104 to providethe PHQ2 questionnaire to the patient via the user device 124. Thepatient PHQ2 input received in response to the PHQ2 questionnaire mayindicate a state of depression of the patient. In some embodiments, thePHQ2 questionnaire may be provided to the patient via the user device124 before scheduling an in-person examination or at any other suitabletime. In some embodiments, the module can escalate the depressiondetection by having the PHQ2 questionnaire be followed by the PHQ9questionnaire if the PHQ2 questionnaire reflects that the patient maylikely be depressed. Additionally, any other similar questionnaire thatassesses depression can be employed as well.

The graded escalation module 110 may compare the patient PHQ2 input to aPHQ2 baseline value. If a difference between the patient PHQ2 input andthe PHQ2 baseline value exceeds a PHQ2 threshold value (e.g., indicatesthat the patient is experiencing severe depression), the gradedescalation module 110 may generate and provide the alert to thephysician or care-provider. The alert may include the quality of healthresult that indicates that the difference between the patient PHQ2 inputand the PHQ2 baseline value exceeds the PHQ2 threshold value and thatthe patient is likely to experience an acute event in the near futureand should have additional in person examination performed by thephysician and/or care-provider as soon as possible. In some embodiments,the PHQ2 value can be replaced by PHQ9 value when the depressiondetection is escalated by having the PHQ2 questionnaire be followed bythe PHQ9 questionnaire. Additionally, as mentioned earlier, any othersimilar questionnaire that assesses depression can be employed as well.

If the difference between the patient PHQ2 input and the PHQ2 baselinevalue does not exceed the PHQ2 threshold value, the graded escalationmodule 110 may repeat the acquisition of patient PHQ2 input after aspecified period of time has elapsed since patient PHQ2 input was lastobtained from the patient. The specified period of time may be equal toor greater than two weeks. In some embodiments, the specified period oftime may be less than two weeks. Additionally, the triggers can begenerated using or in terms of PHQ9 or any similar depressionquestionnaire.

In some embodiments, if the total number of physical unhealthy days doesnot equal zero but does not exceed the physical unhealthy days thresholdvalue but the recent VAS score and/or the recent VAS data indicates thatthe patient is experiencing significant amounts of pain, the gradedescalation module 110 may yet generate and provide the alert to thephysician or care-provider. The alert may include the quality of healthresult that indicates that the total number of physical unhealthy daysdoes not equal zero but does not exceed the physical unhealthy daysthreshold value and that the patient is experiencing significant amountsof pain and given the high pain level interpreted in terms of data ofsimilar patients, the patient is likely to experience an acute event inthe near future and should have additional in person examinationperformed by the physician and/or care-provider as soon as possible.

If the total number of physical unhealthy days and the total number ofmental unhealthy days does not exceed the corresponding threshold value.The graded escalation module 110 may repeat the acquisition of thepatient HRQOL input after the specified period of time has elapsed sincepatient HRQOL input was last obtained from the patient. The specifiedperiod of time may be equal to or greater than two weeks. In someembodiments, the specified period of time may be also less than twoweeks.

In some embodiments, the graded escalation module 110 may be configuredto periodically monitor the health of the patient by collecting andmonitoring multiple iterations of the quality of health result. If thefirst quality of health marker and the second quality of health markerincluded in the quality of health result indicate that the patient isnot likely to experience an acute event in the near future, the qualityof health result may be stored in the memory 117 and/or the gradedescalation module 110 as a first quality of health result. After thespecified period of time, the graded escalation module 110 may repeatthe steps described above and may generate a second quality of healthresult, which may include a current first quality of health marker and acurrent second quality of health marker. The specified period of timemay be equal to or greater than two weeks. In some embodiments, thespecified period of time may be less than two weeks.

If either the current first quality of health marker or the currentsecond quality of health marker indicates that that the patient islikely to experience an acute event in the near future, the alert may begenerated and provided to the physician and/or care-provider. The alertmay include the current first quality of health marker and/or thecurrent second quality of health marker. Additionally, the alert mayindicate that the patient should have additional in person examinationperformed by the physician and/or care-provider as soon as possible.Alternatively, if the current first quality of health marker and thecurrent second quality of health marker indicate that the patient is notlikely to experience an acute event in the near future, the secondquality of health result may be stored in the memory 117 and/or thegraded escalation module 110.

Additionally, the graded escalation module 110 may be configured tocompare the first quality of health result and the second quality ofhealth result to determine a trend of the health of the patient. Forexample, the graded escalation module 110 may compare the first qualityof health marker included in the first quality of health result to thefirst quality of health marker include in the second quality of healthresult. Likewise, the graded escalation module 110 may compare thesecond quality of health marker included in the first quality of healthresult to the second quality of health marker included in the secondquality of health result.

The trend of the health of the patient may indicate whether the firstquality of health markers and/or the second quality of health markers inthe first quality of health result and the second quality of healthresult are the same/similar or are different. If the first quality ofhealth markers and/or the second quality of health markers in the firstquality of health result and the second quality of health result are thesame/similar, the alert may indicate that the patient is not likely toexperience an acute event in the near future. If the first quality ofhealth markers and/or the second quality of health markers in the firstquality of health result and the second quality of health result aredifferent, the graded escalation module 110 may determine whether thedifference is due to an improvement in the health of the patient (e.g.,the scores associated with the first quality of health marker and/orsecond quality of health marker in the second quality of health resultincreased). If the difference is due to an improvement in the health ofthe patient, the alert may indicate that the patient is not likely toexperience an acute event in the near future.

If the difference is not due to an improvement in the health of thepatient (e.g., the scores associated with the first quality of healthmarker and/or the second quality of health marker in the second qualityof health result decreased), the graded escalation module 110 maydetermine whether the difference exceeds an iteration threshold value.If the difference does not exceed the iteration threshold value, thealert may indicate that the patient is not likely to experience an acuteevent in the near future. For example, a difference of 0.5 or more inthe patient HALex input included in the second quality of health resultcompared to the patient HALex input included in the first quality ofhealth result may indicate that the trend of the health of the patientis worsening. In some embodiments, a difference of 0.03 in patient HALexinput may indicate that the trend of the health of the patient isworsening. If the difference exceeds the iteration threshold value, thealert may indicate that the patient is likely to experience an acuteevent in the near future and should have additional in personexamination performed by the physician and/or care-provider as soon aspossible.

In some embodiments, the graded escalation module 110 may determine ashort-term health compliance (SHC) score. The SHC score may benormalized to a score between zero and one hundred. The SHC score may bebased on the patient HRQOL input being worth one hundred points (orother value); the patient HALex input being worth one hundred points (orother value); or the patient HRQOL input being worth fifty points (orother value) and the patient HALex input being worth fifty points (orother value) for a total of one hundred points (or other value).

In some embodiments, the graded escalation module 110 may determine anactivity of daily living (ADL) value of the patient based on the patientHALex input. The ADL value may be compared to an ADL baseline of thepatient that was previously determined. The ADL baseline may bedetermined based on the EHR data included in the EHR database 128 and/orprevious patient HALex input received by the graded escalation module110. The first quality of health marker may be further based on thecomparison of the ADL value to the ADL baseline.

Additionally or alternatively, portions of the patient HALex input,patient HRQOL input, patient PHQ2 input, or any other patient relateddata may be collected via the sensors 120 and 122. For example, thefirst sensor 120 and/or the second sensor 122 may include anaccelerometer configured to determine movement of the patient, which maybe compared against prior movement of the patient to determine whetherthe patient's movement has increased, decreased, or remained constant,which may be used to determine the ADL value of the patient. As anotherexample, each distinct, regular, activity can be recognized by studyingthe time series being generated from the accelerometer and/or any otherwearable inertial sensors since different activities generate differentpattern of movement in the three-dimensional space. Thus, in context oftime and of activity energy, when sensor output is mapped and analyzedin each of the three dimensions (for example, x, y, z), the pattern thatis repeated in time may be distinct and hence unique. The type ofactivity can be determined by looking at the signature of the timeseries data either completely or partially, when each of the threedimensions are concerned.

As another example, the first sensor 120 and/or the second sensor 122may include a HR monitor configured to determine an HR of the patient,which may be used to determine if the patient is experiencing irregularheartbeats. As yet another example, the first sensor 120 and/or thesecond sensor 122 may include a GPS device configured to determine howmuch the patient is home bound, is exposed to polluted areas, where thepatient eats, and/or is exposed to extreme temperatures either high orlow.

In some embodiments, the mood of the patient may be determined byperforming sensor analysis of the sensor data. The sensor data mayinclude an analysis of facial expressions, balance of sympathetic andparasympathetic nervous system, heart rate, heart rate variability,respiration, Galvanic Skin Response (electro dermal analysis) of thepatient based on at least one of a social interaction of the patientwith a phone or a variation in activities of the patient with respect toa healthy patient emotional profile. Additionally, a heart rate of apatient and/or a heart rate variability of the patient may be analyzedin concert with the facial expressions using a camera (for facial actioncoding-based analysis), the posture of the patient, and/or similarbiomarkers.

In some embodiment, the patient response may be auto-filled via thesensor input as described above and then validated by the patient. Insome embodiment, the sensor derived input(s) can over-ride the patientprovided input(s). In some embodiment, the patient response may be takenas the weighted average of sensor derived input and the patient derivedinput. In some other embodiment, the patient credibility may be given aweightage by the care-provider which may be used to create the compositepatient response by combining patient provided input and input derivedvia sensor. In some embodiment, the physician may be provided patientsensor value that can be compared to patient's response to determine theveracity of the patient's response, and hence create the patientcredibility. In some embodiment, the physician may be provided a rangeof credibility weightage to provide to the patient-derived input so thata range of scores may be computed for the patient and based on thephysician's clinical assessment, the physician can empirically determinethe credibility weightage.

In some embodiment, for each response the patient provides, the patientmay be asked a set of questions specific to that response to validatethe patient provided inputs and guide the patient to make the correctassessment. For example, if a patient is asked if he needs help to dothe daily tasks of living. If the patient's response is YES but theresponse is due to a mistaken understanding of what it means to get helpgetting dressed then the additional questions may serve to clarify hisinput.

In some embodiment, the sensor derived input may be calibrated by astatistical analysis of the expected value from a matching set of users.In some embodiment, the sensor derived input may be calibrated by astatistical analysis of the expected value from a matching set of usersas determined by the physician with or without an analysis of thestatistical information.

The chronic burden module 108 may include software executable by or onthe electronic device 102. For example, the chronic burden module 108may include code stored on the electronic device 102 that may beexecuted line-by-line by the processor of the electronic device 102and/or may be loaded into the memory 117 and executed by the processorof the electronic device 102 to perform or control performance of one ormore operations described herein in connection with the chronic burdenmodule 108. Alternatively or additionally, the chronic burden module 108may be implemented in hardware, e.g., as an ASIC, an FPGA, or otherhardware device configured to perform or control performance of one ormore operations described herein in connection with the chronic burdenmodule 108.

The chronic burden module 108 may be configured to evaluate and stratifya chronic care burden of a patient based on one or more factors that mayinclude responses by the patient to questionnaires provided by thequestionnaire module 104, sensor data collected by the sensors 120 and122, and/or the EHR data included in the EHR database 128. In someembodiments, the chronic care burden of the patient may also be based onthe chronic data included in the chronic disease database 126 and/orresponses to questionnaires provided by the disease calculator 106(e.g., a chronic disease calculator score). Additional or alternatively,the chronic burden module 108 may determine whether the patient inlikely to experience a rise in the chronic care burden associated withone or more chronic diseases (e.g., admission to an intensive care unit(ICU) and/or hospitalization) in the intermediate future (e.g., in sixto twelve months).

The chronic burden module 108 may be configured to determine the chroniccare burden of the patient in one or more areas of assessment. Thismodule scores on an ordinal system (say, no risk to very high risk) thefactors that are well known to lead to the most common chronic diseasesand also the factors that may act as an impediment in obtaining promptand appropriate healthcare. For example, the one or more areas ofassessment may include clinical symptom factors, service access factors,risk factors, extenuating factors, psycho-social factors, and/or changereadiness factors. Clinical symptom factors may include COPD, CHF,dementia, depression, or any other appropriate clinical symptom factor.Service access factors may include hospital admissions, self-care,general practitioner follow up, or any other appropriate service accessfactor. Risk factors may include smoking, obesity, BP, HBA1C, drug use,alcohol abuse, recent hospitalization or any other appropriate riskfactor. Extenuating factors may include pain, stress, wounds, or anyother appropriate extenuating factor. Psycho-social factors may includefinancial, transport, disability, or any other appropriate psycho-socialfactor. Change readiness factors may include action relapse or any otherappropriate change readiness factor.

The chronic burden module 108 may receive the EHR data related to thepatient included in the EHR database 128. The chronic burden module 108may determine a biological profile, a psychological profile, and/or asocial profile of the patient based on the EHR data. The biologicalprofile, psychological profile, and/or social profile of the patient mayinclude physician notes regarding the different profiles of the patient.

The EHR data may include factors at a population level, a practicelevel, and/or a patient level to provide varying levels of CCB scoregranularity. Additionally, the EHR data may include data related toweight, height, BMI of the patient and BP of the patient. The EHR datamay also include extenuating factors of the patient such as pain of thepatient. The EHR data may additionally include psycho-social factors ofthe patient such as financial status of the patient. The chronic burdenmodule 108 may include a list of chronic conditions that the patient isexperiencing based on the EHR data.

In some embodiments, the chronic burden module 108 may provide theavatar to the patient via the user device 124. The chronic burden module108 may determine the amount of pain the patient is experiencing, whereon the body that patient is experiencing pain, and/or whether the natureof pain is neuropathic, musculoskeletal or visceral organ based onpatient input provided on the avatar and by using the VAS score includedin the patient input. In some embodiments, the VAS score may be betweenzero and ten, which may provide higher resolution of the CCB score.

In some embodiments, the chronic burden module 108 may direct thequestionnaire module 104 to present one or more questionnaires relatedto the quality of health of the patient to the patient via the userdevice 124. The chronic burden module 108 may adjust the number of thequestionnaires and/or the number of questions that are included in thequestionnaires that are provided to the patient based on previous CCBscores or any additional information collected via interactions with thecaregiving staff. The number of the questionnaires and/or the number ofquestions that are provided to the patient may be determined so as toreduce the number of clicks (e.g., responses to questions) by creatingminimum number of categories (e.g., categories of health-related factorsthat influence CCB score) to determine the CCB score of the patient. Thequestions that are included in the questionnaire may be sorted intocategories such as “Your Biological Risk Factors”, “Your Readiness toChange Health Habits”, “Your Capacity of Self-Care”, and/or “Your SocialLife.” Each category may only include questions that pertain to thecategory. One questions may ask the patient to only identify whichcategories the patient has experienced changes. In some embodiments, theminimum number of clicks may be two clicks since the patient may onlyidentify a single category in which the patient has experienced a changeand then click a question corresponding to the category where the changehas been experienced. Alternatively or additionally, adjusting thenumber of questionnaires and/or questions that are provided to thepatient as warranted, and as directed by the chronic burden module 108,may improve the functioning of the electronic device 102, the network118, and/or the user device 124 as described above.

In some embodiments, the questions may include: “How do you feel?” “Howare your health problems affecting you?” “How are your health problemsaffecting your ability/disability?” “How are your health problemsaffecting your functional capacity, independence, or other aspects ofyour health and well-being?” In these and other embodiments, thequestionnaires may include the HALex, HRQOL, PHQ2, and/or any otherappropriate questionnaire for determining the quality of health of thepatient. Additionally or alternatively, the questions may include “Whatis your current weight, BP, glucose level, and/or waist to hip ratio(WHR)?”

The chronic burden module 108 may receive the sensor data from thesensors 120 and 122 related to the quality of health of the patient. Thesensor data may include information regarding the state of depression,hypertensions, stroke risk, COPD, diabetes, fall risk, and/or any otherappropriate sensor data related to the quality of health of the patient.For example, the sensor data may also include GPS data for determiningservice access such as ER visits, general physician (GP) follow-ups.Additionally, the sensor data may include data related to risk factorssuch as smoking and inactivity of the patient. COPD risk factors such asBP and psycho-social factors (e.g., depression) may be determined usingbio-sensors.

The chronic burden module 108 may determine the CCB score of the patientand a pre-defined risk stratification based on at least two of the EHRdata related to the patient, the sensor data, the patient input providedon the avatar, and/or the patient input received in response to thequestionnaires. The CCB score may be broken down into one or morecategories. For example, the CCB score may include one or morecategories of a risk factor category, a social life category, and anapproach toward health maintenance of the patient category. The CCBscore may be normalized to a score between zero and one hundred or otherrange or values.

The pre-defined risk stratification may include a baseline score of oneor more risk categories based on multiple factors. For example, thepre-defined risk stratification may be based on the chronic dataincluded in the chronic disease database 126 related to patients thathave similar health issues as the patient, patients that have similardemographic characteristics, patients that have similar bio-profiles,and/or any other appropriate patient characteristics that may be used todetermine a baseline.

The chronic burden module 108 may determine a risk stratification of thepatient based on the pre-defined risk stratification. The riskstratification may indicate a present health risk assessment of thepatient (e.g., whether the patient is likely to experience a rise in thechronic care burden associated with one or more chronic diseases in theintermediate future). The risk stratification may include one or more ofthe following risk categories: urgent; very high; high; moderately high;moderate; and/or low.

The chronic burden module 108 may generate and provide a CCB result tothe physician and/or care-provider. The CCB result may include the CCBscore and the risk stratification of the patient. If the riskstratification of the patient is urgent, very high, high, or moderatelyhigh, the CCB result may also indicate that the patient should haveadditional in person examination performed by the physician and/orcare-provider as soon as possible. Alternatively, if the riskstratification of the patient is moderate or low, the CCB result may bestored in the memory 117 and/or the chronic burden module 108 as a firstCCB result. After the specified period of time, the chronic burdenmodule 108 may repeat the steps described above and may generate asecond CCB result. The specified period of time may be equal to orgreater than two weeks. In some embodiments, the specified period oftime may be less than two weeks.

The chronic burden module 108 may compare the first CCB result to thesecond CCB result to determine a trend of the health of the patient. Ifthe trend of the health of the patient is improving, the second CCBresult may be provided to the physician and/or care-provider and may bestored in the memory 117 and/or the chronic burden module 108. If thetrend of the health of the patient is declining, an alert including thesecond CCB result may be generated and provided to the physician and/orcare provider. The alert may indicate that the trend of the health ofthe patient is declining and that the patient should have additional inperson examination performed by the physician and/or care-provider assoon as possible.

In some embodiments, the chronic burden module 108 may direct thequestionnaire module to provide a general self-reported health (GSRH)question to the patient via the user device 124. A GSRH score may bedetermined based on patient GSRH input received in response to the GSRHquestion along a scale of zero to ten and/or the VAS score.Alternatively, the GSRH score may include a health status of the patientof excellent, very good, good, fair, or poor. The GSRH score may also belabeled as excellent, very good, average, poor, very poor.

The chronic burden module 108 may combine the CCB score and the GSRHscore. The risk stratification may be determined based on the combinedscore of the CCB score and the GSRH score. In some embodiments, if thepatient provides a low GSRH score and includes a high CCB score, thepatient may be more likely to be at both a high acute risk as well as ahigh chronic care burden risk.

In some embodiments, the chronic burden module 108 may direct thequestionnaire module 104 to provide the HRQOL questionnaire to thepatient via the user device 124. The patient HRQOL input received inresponse to the HRQOL questionnaire may include the total number ofphysical unhealthy days and/or mental unhealthy days of the patient.Additionally or alternatively, the chronic burden module 108 may directthe questionnaire module to provide the HALex questionnaire to thepatient via the user device 124. The patient HALex input received inresponse to the HALex questionnaire may indicate whether the patient hasrecently experienced a limitation in activity. Additionally oralternatively, the patient HALex input may include an activitylimitation score indicating the limitation in activity. The activitylimitation score may be based on a particular level of disability asindicated by the patient. Multiple questions may be provided to thepatient to determine whether the patient is experiencing limitations inperforming various tasks. For example, whether the patient isexperiencing limitations performing the basic tasks for daily survival;tasks for daily chores; tasks to join work force due to physical,mental, or emotional problems; tasks in the work the patient is doingdue to physical, mental, or emotional problems; or tasks in any aspectof life due to physical, mental, or emotional problems. Each questionmay include an ADL weight value. For example, the patient isexperiencing limitations performing the basic tasks for daily survivalmay be assigned an ADL weight of 0; tasks for daily chores may beassigned an ADL weight of 0.2; tasks to join work force due to physical,mental, or emotional problems may be assigned an ADL weight of 0.4;tasks in the work the patient is doing due to physical, mental, oremotional problems may be assigned an ADL weight of 0.65; or tasks in inany aspect of life due to physical, mental, or emotional problems may beassigned an ADL weight of 0.8. The ADL weight may be combined with PHcoefficients determined based on GSRH questionnaires. For example, aGSRH score of excellent (top rank) may be assigned a PH coefficient of1.0, very good (second rank) may be assigned a PH coefficient of 0.85,good (third rank) may be assigned a PH coefficient of 0.7, (second tothe poor) may be assigned a PH coefficient of 0.3, and poor (worst rank)may be assigned a PH coefficient of 0. The activity limitation score inthis example may be determined according to equation 1:

Activity Score=0.1+0.9(0.41*PH+0.41*ADL Weight+0.18*PH*ADLWeight)  [Equation 1]

In Equation 1, PH may be the PH coefficient from the GSRH score and ADLweight may be the ADL weight in response to the multiple questionsprovided to the patient to determine whether the patient is experiencinglimitations in performing various tasks.

The chronic burden module 108 may combine the CCB score with the patientHRQOL input and/or the patient HALex input. The risk stratification ofthe patient may be determined based on the combination of the CCB scorewith the patient HRQOL input and/or the patient HALex input.

In some embodiments, the chronic burden module 108 may direct thedisease calculator 106 to provide one or more questionnaires to thepatient via the user device 124. The chronic burden module 108 maydetermine the disease calculator score based on the responses to thequestionnaires provided by the disease calculator 106. The chronicburden module 108 may combine the CCB score with the disease calculatorscore. The risk stratification of the patient may be determined based onthe combination of the CCB score with the disease calculator score. Insome embodiments, the risk stratification of the patient determinedbased on the combination of the CCB score with the disease calculatorscore may indicate impending chronic care diseases with a risk of suddendeath (e.g., a sudden death burden).

The compliance to a recommended lifestyle health choice of a patient maybe analyzed in the lifestyle choice module 114, which may includesoftware executable by or on the electronic device 102. For example, thelifestyle choice module 114 may include code stored on the electronicdevice 102 that may be executed line-by-line by the processor of theelectronic device 102 and/or may be loaded into the memory 117 andexecuted by the processor of the electronic device 102 to perform orcontrol performance of one or more operations described herein inconnection with the lifestyle choice module 114. Alternatively oradditionally, the lifestyle choice module 114 may be implemented inhardware, e.g., as an ASIC, an FPGA, or other hardware device configuredto perform or control performance of one or more operations describedherein in connection with the lifestyle choice module 114.

The lifestyle choice module 114 may be configured to evaluate andstratify compliance to a recommended lifestyle health choice of apatient based on one or more factors that may include responses by thepatient to questionnaires provided by the questionnaire module 104,sensor data collected by the sensors 120 and 122, and/or the EHR dataincluded in the EHR database 128. In some embodiments, the lifestylehealth compliance of the patient may also be based on the chronic dataincluded in the chronic disease database 126 and/or responses toquestionnaires provided by the disease calculator 106.

The lifestyle choice module 114 may be configured to determine whetherthe patient has experienced a change in lifestyle choice, for good orbad, in one or more categories over a period of time. In someembodiments, the period of time may include the period of time since thepatient last had an in-person examination performed by the physicianand/or care provider. The one or more categories may include dietaryhealth choice; daily routine; dietary harm avoidance; organized physicalactivities; and/or social and mental balance. The lifestyle choicemodule 114 may determine a healthy lifestyle choice compliance (LCC)score based on changes in the lifestyle of the patient, which mayindicate internal control of health of the patient. A LCC score of onehundred indicates full compliance to lifestyle choice prescriptions anda LCC score of zero indicates complete lack of compliance to thelifestyle choice prescriptions.

The lifestyle choice module 114 may have provision to provide ordetermine the classifiability, proportionality, and/or adaptability ofthe LCC score. Classifiabiltiy of the LCC score may be based on thechronic diseases of the patient. For example, if the patient hasdiabetes, the LCC score may be classified based on questions, data(possibly including from sensors), and/or patient input related todiabetes. Proportionality of the LCC score may be achieved by assigningcategories that are more important for the particular case of thepatient. For example, the patient may be compliant with dietary healthychoices, but may not be compliant with dietary harm avoidances and assuch that category may be given greater weight using a VAS scale-basedslider. Adaptability of the LCC score may be achieved by varying the LCCscore in ordinal manner. For example, if a patient receives a score offour for exercising six days one week, the patient will receive a scoreof two for exercising three days a subsequent week.

The lifestyle choice module 114 may receive the EHR data related to thepatient included in the EHR database 128. The lifestyle choice module114 may determine a biological profile, a psychological profile, and/ora social profile of the patient based on the EHR data included in theEHR database 128. Additionally, or alternatively, the lifestyle choicemodule 114 may determine a previous lifestyle choice prescription of thepatient based on the EHR data.

The lifestyle choice module 114 may direct the questionnaire module 104to provide one or more questionnaires to the patient via the user device124. For example. The lifestyle choice module 114 may direct thequestionnaire module 104 to provide the HLPCQ questionnaire to thepatient. Additionally, or alternatively, the lifestyle choice module 114may direct the questionnaire module 104 to provide the question “How doyou rate the efforts you make to take care of your health?”

The lifestyle choice module 114 may adjust the number of thequestionnaires and/or the number of questions that are included in thequestionnaires that are provided to the patient based on previouslifestyle choice prescriptions, as warranted. Adjusting the number ofquestionnaires and/or questions that are provided to the patient aswarranted, and as directed by the lifestyle choice module 114, mayimprove the functioning of the electronic device 102, the network 118,and/or the user device 124 as described above.

Additionally, or alternatively, the lifestyle choice module 114 may askthe patient if the patient has experienced any changes in the one ormore categories (e.g., dietary health choice; daily routine; dietaryharm avoidance; organized physical activities; and/or social and mentalbalance). The number of the questionnaires and/or the number ofquestions that are included in the questionnaires that are provided tothe patient may be determined so as to reduce a number of clicks (e.g.,responses to questions) and a minimum number of categories (e.g.,categories of health-related factors) to determine a lifestyle choiceprescription or update the lifestyle choice prescription of the patient.In some embodiments, the minimum number of clicks may include fifteen orless clicks. Alternatively, the minimum number of clicks may includemore than fifteen clicks.

The lifestyle choice module 114 may receive patient HLPCQ input inresponse to the HLPCQ questionnaire. The patient HLPCQ input may includeresponses to each of the questions indicating whether adherence to thequestion is strong, moderate, low, or absent. If the adherence is low orabsent, the specified period of time between determinations of thelifestyle choice prescription may be reduced. For example, the specifiedperiod of time between determinations of the lifestyle choiceprescription may be reduced to once every six weeks. The patient HLPCQinput may include response to the question of how do you rate theefforts you make to take care of your health as either Excellent, verygood, good, fair, or poor.

Additionally or alternatively, the lifestyle choice module 114 maydirect the questionnaire module 104 to provide a questionnaire to assessthe lifestyle risk score (LRSQ) via the user device. The LRSQ responsemay indicate habits of the patient that may be harmful. For example,smoking, alcohol, dietary harm, sedentary lifestyle, scheduleirregularity, quality of social, and/or stress management of thepatient.

The lifestyle choice module 114 may receive the sensor data from thesensors 120 and 122 related to the quality of health of the patient. Thesensor data may include information regarding lifestyle activitiesrelated to one or more chronic diseases of the patient. The sensor datamay quantify compliance of the patient with the lifestyle choiceprescriptions within a range of values between zero and one.

The lifestyle choice module 114 may determine the LCC score based on thesensor data, the patient LRSQ input, and/or the patient HLPCQ input. Forexample, each response included in the patient HLPCQ input may beassigned a weight of four (e.g., four points for strong, three pointsfor moderate, two points for low, and one point for absent adherence).The total number of responses may be twenty five and the LCC score maybe determined out of one hundred points. Alternatively, each responseincluded in the patient HLPCQ input may be individually weighted basedon the chronic diseases or the individual assessment of the patient. Forexample, adherence to medication lifestyle prescription may be weightedhigher than other lifestyle prescriptions. Virtually in the advancedstages of most chronic diseases, there may be disease specific lifestyleconstraints. Thus, a system that is directed towards elderly may accountfor personalization. The total score may be out of 100 but theirweightage may be broken down over a smaller or larger number ofquestions. For example, if there are only twenty questions in thelifestyle questionnaire that are deemed relevant then the weight of eachquestion may be five.

In some embodiments, the LCC score may include multiple categories, suchas permanent and controllable. The permanent category may includepermanent factors such as the age, the race, the gender, the existenceof certain chronic diseases, the health history of the patient. Thecontrollable category may include factors related to a patient'sapproach towards health care such as diet, exercise, disciplined dailyroutine, dietary harm avoidance, etc.

In some embodiments, the lifestyle choice module 114 may determine oneor more lifestyle choice prescription compliance targets for thepatient. Each of the lifestyle choice prescriptions targets may beassigned a separate weighted score from zero to one. The weighted scoresmay be assigned based on importance of the chronic disease and/or thelifestyle choice prescription.

The lifestyle choice module 114 may generate a lifestyle choice resultthat includes the LCC score of the patient. The lifestyle choice module114 may provide the lifestyle choice result to the physician and/or careprovider. The lifestyle choice module 114 may repeat the steps discussedabove after the specified period of time has elapsed since a lifestylechoice prescription was last obtained from the patient. In someembodiments, the specified period of time may be between two and threemonths. Alternatively, the specified period of time may be greater thanthree months or less than two months. The length of the specified periodmay be based on the urgency of the lifestyle intervention and thus canbe as less as even one week.

In some embodiments, the patient input (e.g., patient responses to thequestionnaires) may be scaled within a range of zero to one and the LCCscore may be based on the scaled patient input. In these and otherembodiments, a score of one equals excellent adherence and a score ofzero equals absent adherence to the corresponding lifestyle choiceprescription. Others ranges or values may be used in other embodiments.

In some embodiments, the lifestyle choice module 114 may receive thechronic data from the chronic disease database 126. The chronic data mayinclude LCC scores for patients that are similar to the patient (e.g.,similar patients). For example, the patients may be a similar age,weight, gender, race, or any other appropriate characteristic fordetermining patients are similar. As another example, the patients maybe experiencing the same chronic diseases. The lifestyle choice module114 may compare the LCC score of the patient to the LCC scores includedin the chronic data. The lifestyle choice result may also include astatistical rank of the patient compared to the patients included in thechronic data.

In some embodiments, if the LCC score of the patient falls below apre-specified limit, the lifestyle choice module 114 may generate andprovide an alert to the physician and/or care provider. The alert mayinclude the LCC score and may indicate that the LCC score of the patientfell below the pre-specified limit and that the patient should haveadditional in person examination performed by the physician and/orcare-provider as soon as possible. The pre-specified limit may bedetermined based on the patient input and/or the sensor data. In someembodiments, the pre-specified limit may be determine based on physicianpreferences. For example, the pre-specified limit may be set to onehundred fifty minutes of exercise a week, eight hours of sleep a night,or sleeping before midnight. Additionally or alternatively, thepre-specified limit may be based on a number of standard deviations ofchange that is observed below the pre-specified limit.

The total health module 116 may include software executable by or on theelectronic device 102. For example, the total health module 116 mayinclude code stored on the electronic device 102 that may be executedline-by-line by the processor of the electronic device 102 and/or may beloaded into the memory 117 and executed by the processor of theelectronic device 102 to perform or control performance of one or moreoperations described herein in connection with the total health module116. Alternatively or additionally, the total health module 116 may beimplemented in hardware, e.g., as an ASIC, an FPGA, or other hardwaredevice configured to perform or control performance of one or moreoperations described herein in connection with the total health module116.

The total health module 116 may be configured to generate a health riskscore of the patient. The health risk score may be based on responses bythe patient to questionnaires provided by the questionnaire module 104,sensor data collected by the sensors 120 and 122, the SHC scoredetermined by the graded escalation module 110, the CCB score determinedby the chronic burden module 108, the LCC score determined by thelifestyle choice module 114, and/or the EHR data included in the EHRdatabase 128. In some embodiments, the health risk score may also bebased on the chronic data included in the chronic disease database 126.

The total health module 116 may determine the health risk score so as toindicate the short-term health (e.g., the risk of the patientexperiencing an acute event in the near future), the chronic burden orCCB (e.g., the risk of the patient being admitted to an ICU and/orhospitalization in the intermediate future), and lifestyle choice (e.g.,adherence to one or more lifestyle choice prescriptions) of the patient.The short-term health may include a first dimension of the health riskscore. The CCB score may include a second health dimension of the healthrisk score. The lifestyle choice may include a third health dimension ofthe health risk score.

In some embodiments, each dimension of the health risk score of thepatient may include a max value of one hundred points with the healthrisk score including a max value of three hundred points. Alternatively,each of the score may be assigned out of one thousand. Or, the compositescore may be assigned out of one thousand. Alternatively, each dimensionof the health risk score of the patient may include more or less thanone hundred points. For example, the LCC score may include the dietaryhealth choices worth twenty points, the daily routine choices worthtwenty points, the dietary harm avoidance choices worth twenty fourpoints, the organized physical activity choices worth sixteen points,and the social and mental balance choices worth twenty points. Asanother example, the SHC score may include the patient HRQOL input worthone hundred points; the patient HALex input worth one hundred points; orthe patient HRQOL input worth fifty points and the patient HALex inputworth fifty points for a total of one hundred points. As yet anotherexample, the CCB score in some embodiments may be calculated out offifty but then may be multiplied by two to normalize the score betweenzero and one hundred.

In some embodiments, the total health module 116 may generate the SHCscore by repeating the same or similar steps discussed above in relationto the graded escalation module 110 generating the SHC score. The totalhealth module 116 may also generate the CCB score by repeating the sameor similar steps discussed above in relation to the chronic burdenmodule 108 generating the CCB score. Likewise, the total health module116 may generate the LCC score by repeating the same or similar stepsdiscussed above in relation to the lifestyle choice module 114generating the LCC score. Additionally or alternatively, the totalhealth module 116 may obtain the SHC score from the graded escalationmodule 110, the CCB score from the chronic burden module 108, and/or theLCC score from the lifestyle choice module 114.

The total health module 116 may receive the sensor data from the sensors120 and 122 related to the quality of health of the patient. The sensordata may include information regarding the state of depression,hypertensions, risk stroke, COPD, diabetes, fall risk, and/or any otherappropriate sensor data related to the quality of health of the patient.For example, the sensor data may include GPS data for determiningservice access such as ER visits, GP follow-ups.

The total health module 116 may direct the questionnaire module 104 toprovide one or more questionnaires to the patient via the user device124. For example. The total health module 116 may direct thequestionnaire module 104 to provide the HLPCQ, HRQOL, PHQ2, HLPCQ, orany other appropriate questionnaire to the patient.

The total health module 116 may adjust the number of the questionnairesand/or the number of questions that are included in the questionnairesthat are provided to the patient based on previous health risk scores,as warranted. Adjusting the number of questionnaires and/or questionsthat are provided to the patient as warranted, and as directed by thetotal health module 116, may improve the functioning of the electronicdevice 102, the network 118, and/or the user device 124 as describedabove.

Additionally or alternatively, the total health module 116 may ask thepatient if the patient has experienced any changes in the one or morecategories (e.g., short-term health, chronic burden, and/or lifestylechoices). The number of the questionnaires and/or the number ofquestions that are included in the questionnaires that are provided tothe patient via the user device 124 may be determined so as to reduce anumber of clicks (e.g., responses to questions) and a minimum number ofcategories (e.g., categories of health related factors) to determine thehealth risk score of the patient. In some embodiments, the minimumnumber of clicks may be three clicks or less. Alternatively, the minimumnumber of clicks may include more than fifteen clicks.

In some embodiments, the total health module 116 may generate the healthrisk score further based on the sensor data. In these and otherembodiments, the total health module 116 may compare the sensor data toknown baselines indicated in the chronic data included in the chronicdisease database 126. For example, a mean value and/or a standarddeviation of sensor of a similar patient may be used as the knownbaselines. The similar patient may be determined based on similarity ofan age, a gender, a race, a geographic location, an education level, anincome level, a smoking habit, an alcohol habit, and/or a chroniccondition between the patient and the similar patient.

The total health module 116 may categorize the health score for each ofthe three health dimensions which may include short-term health score,chronic care burden, and lifestyle choice score of the patient.

The total health module 116 may determine the minimum category ofquestions for high resolution statistical measurement of each dimensionof the three health dimensions. For example, the questionnaire mayinclude multiple categories such as “Your Biological Risk Factors”,“Your Readiness to Change Health Habits”, “Your Capacity of Self-Care”,and “Your Social Life.” Each category may include questions directed toone or more of the three health dimensions. The minimum number of clicksmay be two clicks since the patient only has to identify the categorywhere a change may have occurred and then click the questions related tothe category where the change has occurred.

The total health module 116 may quantify each of the three healthdimensions of the patient per the VAS health score.

The total health module 116 may generate and provide a health riskresult to the physician and/or care-provider. The health risk result mayinclude the health risk score of the patient, one or more of the minimumcategory of questions for high statistical measurement, and thequantification of each of the three health dimensions of the patient.The health risk result may indicate the likelihood of the patientexperiencing an acute event in the near future, or risk of developmentof a new chronic condition in the near future, or the risk of thepatient with at least one chronic disease experiencing a rise in achronic care burden, and/or the lifestyle health compliance of thepatient.

In some embodiments, the total health module 116 may generate apreferential health score. The preferential health score may be based onweighted scores of one or more of the three health dimensions includedin the patient input using a sliding score for the VAS score. The threedimensions may be weighted differently based on a particular chronicdisease. The health risk result may include the preferential healthscore. For example, a patient that is being incentivized to improvetheir health, the preferential score may include a weighted lifestylechoice score since an improved lifestyle is desired

In some embodiments, the health risk result may be stored in the memory117 and/or the total health module 116 as a first health risk result.After the specified period of time, the chronic burden module 108 mayrepeat the steps described above and may generate a second health riskresult. The specified period of time may be equal to or greater than twoweeks. In some embodiments, the specified period of time may be lessthan two weeks.

The total health module 116 may compare the first health risk result tothe second health risk result to determine a trend of the health of thepatient. If the trend of the health of the patient is improving, thesecond health risk result may be provided to the physician and/orcare-provider and may be stored in the memory 117 and/or the totalhealth module 116. If the trend of the health of the patient isdeclining, an alert including the second health risk result and/or adifference between the first health risk result and the second healthrisk result may be generated and provided to the physician and/or careprovider. The alert may indicate that the trend of the health of thepatient is declining and that the patient should have additional inperson examination performed by the physician and/or care-provider assoon as possible.

In some embodiments, the total health module 116 may perform gradedescalation on the health score for each of the three health dimensionswhere a patient may be considered to be at risk as their score fallsbelow the threshold for the given category In some embodiments, forshort-term health, it may include a change that is more than onestandard deviation below a change observed in that the short-termhealth; for chronic care burden, it may be based on whether the riskscore is greater than thirty out of one hundred; and for the lifestylechoice score, it may also include a change that is more than onestandard deviation below a change in the lifestyle choice.

In some embodiments, the total health module 116 may collect a healthrisk score of a similar patient. For example, the health risk score ofthe similar patient may be based on the chronic data included in thechronic disease database 126 related to patients that have similarhealth issues as the patient, patients that have similar demographiccharacteristics, patients that have similar bio-profiles, and/or anyother appropriate patient characteristic that may be used to determinesimilarity. The total health module 116 may compare the health riskscore of the similar patient to the health risk score of the patient.The health risk score may be based on the comparison of the health riskscore of the similar patient to the health risk score of the patient.

In some embodiments, the total health module 116 may compare each of thethree dimensions of health of the health risk score of the similarpatient to the corresponding dimension of health of the health riskscore of the patient. In these and other embodiments, the health riskscore may be based on the comparison of each dimension of health of thehealth risk score of the similar patient to the corresponding dimensionof health of the health risk score of the patient (e.g., an expectedbaseline).

A weakness of the questionnaire-based health assessment may be in thefact that a patient may deliberately and misleadingly present himself tobe significantly sicker, for example, to obtain earlier appointments. Ifwidely practiced, such an approach can unfortunately dilute the benefitof such health scoring and assessment systems. Thus, the system may alsoencompass a reliability evaluation module (not illustrated). Scorevector v₁ may represent patient's health assessment (or, assessment of akey category such as general self-reported health) on k differentoccasions where he was evaluated by the physician and the vector v₂ mayrepresents the doctor's health assessment (or, assessment of a keycategory such as general self-reported health). Both vectors may be ak-dimensional vector, with entry i represents the score on the i^(th)occasion. The module may compute the difference between the patientassessed health and the doctor's assessed health and based on that maycompute the reliability index which may be used to compute theadjustment index which may correspond to the quantity by which thepatient reported score is adjusted in the future. A larger thedifference between vector v₁ and vector v₂, larger is the adjustmentthat may be applied. One way to compute the adjustment index may bethrough the mean squared difference between the assessments throughusing the L2 norm and dividing by k:

$\epsilon = {\frac{1}{k}{{v_{1} - v_{2}}}_{2}^{2}}$

It may be used

$\frac{1}{\epsilon}$

as our weighting factor to determine a user's “trustworthiness” inself-reported health.

The under-diagnosis module 112 may include software executable by or onthe electronic device 102. For example, the under-diagnosis module 112may include code stored on the electronic device 102 that may beexecuted line-by-line by the processor of the electronic device 102and/or may be loaded into the memory 117 and executed by the processorof the electronic device 102 to perform or control performance of one ormore operations described herein in connection with the under-diagnosismodule 112. Alternatively or additionally, the under diagnosis module112 may be implemented in hardware, e.g., as an ASIC, an FPGA, or otherhardware device configured to perform or control performance of one ormore operations described herein in connection with the under-diagnosismodule 112.

The under-diagnosis module 112 may be configured to evaluate relativerisk of under-diagnosis of a patient based on the EHR data included inthe EHR database 128, the chronic data included in the chronic diseasedatabase 126, and/or the patient HRQOL input. The under-diagnosis module112 may be configured to predict under-diagnosis in one or more chronicdiseases, such as alcohol abuse, arthritis, asthma, cancer,cardiovascular disease, COPD, diabetes, disability, immunization, mentalhealth, nutrition, physical activity, weight status, oral health, and/orother chronic disease(s).

The under-diagnosis module 112 may receive the chronic data from thechronic disease database 126. The chronic data may include data thatindicates a number of patients that are diagnosed with one or morechronic diseases in a geographic location and/or for a givendemographic. The number of patients that are diagnosed with one or morechronic diseases in a geographic location may be based onepidemiological attributes of the patients. For example, the chronicdata may include data indicating whether the patients have experiencedpoor mental and/or physical health in the last day, ten days, thirtydays, or any other appropriate range of time. Additionally, the chronicdata may be adjusted based on statistical data related to age,ethnicity, gender, income level, education level, and/or geographiclocations of the patients.

The under-diagnosis module 112 may receive the EHR data from the EHRdatabase 128. The EHR data may include data indicating the number ofpatients that are diagnosed with one or more chronic diseases in apractice of a physician. Additionally, the EHR data may include datathat indicates the number of patients in the practice of the physicianwith a higher risk for one or more chronic diseases. The under-diagnosismodule 112 may determine whether a patient is at a higher risk or notbased on, e.g., the sensor data related to the quality of health of thepatients collected by the sensors 120 and 122.

The under-diagnosis module 112 may compare the number of patientsdiagnosed with a chronic disease in the practice of the physicianincluded in the EHR data to the number of patients diagnosed with thesame chronic disease in the geographic location included in the chronicdata. The under-diagnosis module 112 may compare and/or analyze all ofthe patients that visit the physician. Additionally or alternatively,the under-diagnosis module 112 may compare the number of patients with ahigher risk for a chronic disease in the practice of the physicianincluded in the EHR data to the number of patients with a higher riskfor the same chronic disease in the geographic location included in thechronic data. For example, the number of patients diagnosed with or athigher risk for the chronic disease in the practice of the physician maybe compared to one or more tables included in some appropriate database,such as the CDC database.

The under-diagnosis module 112 may generate a comparison result. Thecomparison result may indicate whether the number of patients diagnosedwith the chronic disease in the practice of the physician is greaterthan or less than the number of patients diagnosed with the chronicdisease in the geographic location. Additionally or alternatively, thecomparison result may indicate whether the number of patients at ahigher risk for the chronic disease in the practice of the physician isgreater than or less than the number of patients at a higher risk forthe chronic disease in the geographic location or at some selectedpractices of other physicians, clinics or hospitals. In anotherembodiment of the invention, a reference database may be selected tomatch the demographic attributes of the patients of the given physician.

In another embodiment of the invention, the under-diagnosis module 112may include a reference baseline that can evaluate data from one or moresensors configured to provide data related to a quality of health of apatient that pertains to, includes, and/or indicates at least one ofdiet pattern, sleep pattern, exercise pattern, activity level, heartrate, posture, stress, blood pressure variation, blood glucose, heartrhythm, smoking status, pain level, and/or GPS data of the patient. Insome embodiments, sensors or meta-sensors may include a set of sensorsand associated analysis algorithms that provide the required qualitativeor quantitative label.

If the number of patients diagnosed with the chronic disease in thepractice of the given physician is less than the number of patientsdiagnosed with the chronic disease in the geographic location and/or ifthe number of patients at a higher risk for the chronic disease in thepractice of the physician is less than the number of patients at ahigher risk for the chronic disease in the geographic location, theunder diagnosis module 112 may generate and provide the alert to thephysician and/or care provider. The alert may include the comparisonresult and may indicate that the number of patients diagnosed with thechronic disease in the practice of the physician is less than the numberof patients diagnosed with the chronic disease in the geographiclocation and/or if the number of patients at a higher risk for thechronic disease in the practice of the physician is less than the numberof patients at a higher risk for the chronic disease in the geographiclocation or at select practices or clinics or hospitals. In anotherembodiment, the reference database may provide information about thedemographic characteristics of the set of patients that were initiallyunderdiagnosed.

In some embodiments, the EHR data may include data that indicates anumber of patients in the practice of the physician that have undergonea laboratory-based screening test for a chronic disease. In these andother embodiments, the under-diagnosis module 112 may compare the numberof patients diagnosed with the chronic disease in the geographiclocation included in the chronic data to the number of patients in thepractice of the physician that have undergone laboratory-based screeningtest for the same chronic disease included in the EHR data. Thecomparison result may be further based on the comparison of the numberof patients diagnosed with the chronic disease in the geographiclocation to the number of patients in the practice of the physician thathave undergone laboratory-based screening test for the same chronicdisease.

In some embodiments, the EHR data may include data that indicates anumber of patients in the practice of the physician that have beenstratified as having a higher risk for a chronic disease based on aphysician annotated diagnosis. In these and other embodiments, the underdiagnosis module 112 may compare the number of patients diagnosed withthe chronic disease in the geographic location included in the chronicdata to the number of patients in the practice of the physician thathave been stratified as having a higher risk for the same chronicdiseases based on the physician annotated diagnosis included in the EHRdata. The comparison result may also be based on the comparison of thenumber of patients diagnosed with the chronic disease in the geographiclocation to the number of patients in the practice of the physician thathave been stratified as having a higher risk for the same chronicdisease based on the physician annotated diagnosis.

In some embodiments, the EHR data may include data that indicates anumber of patients in the practice of the physician with a higher riskfor a chronic disease using the sensor data related to the quality ofhealth of the patients. In these and other embodiments, the sensor datarelated to the quality of health of the patients may be based on atleast one of a diet pattern, a sleep pattern, and/or an exercise patternof the patients included in the EHR data.

In some embodiments, the EHR data may include data that indicates anumber of patients in the practice of the physician that have beenstratified as having a higher risk for one or more chronic diseasesbased on patient responses to one or more questionnaires provided to thepatient by the disease calculator 106. In these and other embodiments,the under diagnosis module 112 may compare the number of patientsdiagnosed with the same chronic disease in the geographic locationincluded in the chronic data to the number of patients in the practiceof the physician that have been stratified as having a higher risk forthe chronic disease based on patient responses to one or morequestionnaires provided to the patient by the disease calculator 106included in the EHR data. The comparison result may also be based on thecomparison of the number of patients diagnosed with the chronic diseasein the geographic location to the number of patients in the practice ofthe physician that have been stratified as having a higher risk for thesame chronic disease based on patient responses to one or morequestionnaires provided to the patient by the disease calculator 106.

In some embodiments, the EHR data may include data that indicates anumber of patients in the practice of the physician with poor complianceto medical recommendations by the physician (e.g., lifestyle choiceprescriptions generated by the lifestyle choice module 114). In theseand other embodiments, the under-diagnosis module 112 may compare thenumber of patients diagnosed with a chronic disease in the geographiclocation included in the chronic data to the number of patients in thepractice of the physician with poor compliance to the medicalrecommendations by the physician included in the EHR data. Thecomparison result may be further based on the comparison of the numberof patients diagnosed with the chronic disease in the geographiclocation to the number of patients in the practice of the physician withpoor compliance to the medical recommendations by the physician.

In some embodiments, the chronic data may include data that indicatespopulation norms and expected deviation for a chronic disease forshort-term HRQOL of the patients using the patient HALex input includedin the chronic data. In these and other embodiments, the under diagnosismodule 112 may compare the population norms and expected deviation forthe chronic disease for short-term HRQOL using the patient HALex inputincluded in the chronic data to the number of patients diagnosed withthe same chronic disease in the practice of the physician included inthe EHR data. The comparison result may be further based on thecomparison of the population norms and expected deviation for thechronic disease for short-term HRQOL using the patient HALex input tothe number of patients diagnosed with the same chronic disease in thepractice of the physician.

In some embodiments, the chronic data may include data that indicatespopulation norms and expected deviation for a chronic disease forshort-term HRQOL using a VAS HRQOL score. In some embodiments, the VASHRQOL may be based on patients rating their health from zero to onehundred where zero may indicate poor health and one hundred may indicatethe best health a patient can imagine. In other embodiments, the VASHRQOL may be based on a score of negative twenty to twenty wherenegative twenty may indicate a health status worse than death,Additionally, these scores may be normalized from zero to one hundred.For example, twenty may be added to all the scores and then the scoresmay be scaled back in the range zero to one hundred. The populationlevel norms may be obtained by collecting the scores over a sufficientlylarge sample such that there is statistically enough patients in eachcategory of the score. The categories may include an age, a gender, arace, a geographic location, an education level, an income level, asmoking habit, an alcohol habit, and/or a chronic condition of thepatients. In these and other embodiments, the under diagnosis module 112may compare the population norms and expected deviation for the chronicdisease for short-term HRQOL using the VAS HRQOL score included in thechronic data to the number of patients diagnosed with the same chronicdisease in the practice of the physician included in the EHR data. Thecomparison result may be further based on the comparison of thepopulation norms and expected deviation for the chronic disease forshort-term HRQOL using the VAS HRQOL score to the number of patientsdiagnosed with the same chronic diseases in the practice of thephysician.

FIG. 2 is a flow diagram of an example method 200 to generate a totalhealth score of a patient, arranged in accordance with at least oneembodiment described herein. The method 200 may be performed, in wholeor in part, by an electronic device such as the electronic device 102 ofFIG. 1, the sensors 120, 122, the user device 124, and/or one or moreother systems or devices. In some embodiments, the electronic device mayhave access to a chronic disease database, such as the chronic diseasedatabase 126 of FIG. 1 and/or an EHR database such as the EHR database128 of FIG. 1. Alternatively or additionally, the electronic device mayhave access to one or more sensors, such as the sensors 120 and 122 ofFIG. 1.

The method 200 may be performed, in whole or in part, by the electronicdevice. Alternatively or additionally, the method 200 may be implementedby a processor device that performs or controls performance of one ormore of the operations of the method 200. For instance, a computer (suchas a computing device 1400 of FIG. 14) or other processor device may becommunicatively coupled to the electronic device and/or may be includedas a control system of the electronic device and may execute software orother computer-readable instructions accessible to the computer, e.g.,stored on a non-transitory computer-readable medium accessible to thecomputer, to perform or control the electronic device to perform themethod 200 of FIG. 2.

The method 200 may include one or more of blocks 202, 204, 206, 208,210, 212, 214, 216, 218, 220, 222, 224, and/or 226. Although illustratedas discrete blocks, various blocks may be divided into additionalblocks, supplemented with additional blocks, combined into fewer blocks,or eliminated, depending on the particular implementation. The method200 may begin at block 202.

In block 202 (“Obtain A Full Medical Profile of A Patient”) a fullmedical profile of a patient may be obtained. The full medical profileof the patient may be based on the EHR data, such as the EHR dataincluded in the EHR database 128 of FIG. 1. Block 202 may be followed byblock 204.

In block 204 (“Determine A Functional Health Of The Patient”), afunctional health of the patient may be determined. In an example, thefunctional health of the patient may be determined based on patientHALex input as discussed elsewhere herein. Block 204 may be followed byblock 206.

In block 206 (“Determine A Pain State And A Mental State Of ThePatient”), a pain state and a mental state of the patient may bedetermined. In an example, the pain state of the patient may bedetermined based on patient input provided on an avatar as discussedelsewhere herein. The mental state of the patient may be determinedbased on patient PHQ2 input as discussed elsewhere herein. Block 206 maybe followed by block 208.

In block 208 (“Is The Patient Trending Towards Urgent”), it may bedetermined whether the patient is trending towards urgent. If thepatient is trending towards urgent, block 208 may be followed by block210. If the patient is not trending towards urgent, block 208 may befollowed by block 212.

In block 210 (“Send Notice Care-Provider”), a notice may be sent to thecare-provider. In some embodiments, the care-provider may include aphysician of the patient. The notice (e.g., the alert) may include thequality of health result. The notice may indicate that the patient islikely to experience an acute event in the near future and should haveadditional in person examination performed by the physician and/orcare-provider as soon as possible.

In block 212 (“Generate A Quality Of Health Result”), a quality ofhealth result may be generated. The quality of health result may bebased on the pain state, mental state, functional health, and/or fullpatient set of the patient. The quality of health result may be the sameor similar to the SHC result discussed elsewhere herein. Block 212 maybe followed by block 226.

In block 214 (“Determine Overall Chronic Burden Score Of The Patient”),an overall chronic burden score (e.g., a CCB score) of the patient maybe determined. The overall chronic burden score may be the same orsimilar to the CCB score discussed elsewhere herein. The overall chronicburden score may be based on all chronic diseases the patient isdiagnosed with. Alternatively or additionally, the overall chronicburden score may be based on the pain state and the mental state of thepatient determined in block 206. Block 214 may be followed by block 216.

In block 216 (“Determine A Chronic Burden Score For Each Of One Or MoreChronic Diseases”), an individual chronic burden score for each of oneor more chronic diseases may be determined. Each individual chronicdisease burden score may be determined based on a single chronic diseasethat the patient is experiencing. The individual chronic burden scorefor each of the one or more chronic diseases may be similar to theoverall chronic burden score but related to a single chronic disease.Block 216 may be followed by block 226.

In block 218 (“Determine A Function Specific Lifestyle choicePrescription For The Patient”), a function specific lifestyle choiceprescription for the patient may be determined. The function specificlifestyle choice prescription may be determined based on the functionalhealth of the patient determined in block 214. The function specificlifestyle choice prescription may be the same or similar to thelifestyle choice prescription generated by the lifestyle choice module114 of FIG. 1. Block 218 may be followed by block 220.

In block 220 (“Determine One Or More Specific Lifestyle ChoicePrescriptions For The Patient”), one or more specific lifestyle choiceprescriptions for the patient may be determined. The one or morespecific lifestyle choice prescriptions may be directed to reduce animpact that one or more chronic diseases are having on the patient. Theone or more specific lifestyle choice prescriptions for the patient maybe determined based on the overall chronic burden score and/or theindividual chronic burden score for each of the one or more chronicdiseases determined in block 216. Block 220 may be followed by block222.

In block 222 (“Receive Patient Input That Indicates Patient Control AndPerceived Lifestyle Health Of The Patient”), patient input thatindicates patient control and perceived lifestyle health of the patientmay be received. The patient input may include the patient HLPCQ inputand/or the patient LRSQ input discussed elsewhere herein. Block 222 maybe followed by block 224.

In block 224 (“Generate A LCC Result”) a LCC result may be generated.The LCC result may include an LCC score. The LCC result and/or the LCCscore may be the same or similar to the LCC result and/or LCC scoregenerated by the lifestyle choice module 114 of FIG. 1. Block 224 may befollowed by block 226.

In block 226 (“Determine A Total Health Score For The Patient”), a totalhealth score for the patient may be determined. The total health scoremay be determined based on the quality of health result generated inblock 212, the chronic burden score for the one or more chronic diseasesdetermined in block 216, and/or the LCC result determined in block 224.The total health score may be the same or similar to the health riskscore generated by the total health module 116 of FIG. 1.

One skilled in the art will appreciate that, for this and otherprocesses, operations, and methods disclosed herein, the functionsand/or operations performed may be implemented in differing order.Furthermore, the outlined functions and operations are only provided asexamples, and some of the functions and operations may be optional,combined into fewer functions and operations, or expanded intoadditional functions and operations without detracting from the essenceof the disclosed embodiments.

FIG. 3 is a graphical representation 300 of a number of Medicarepatients that are not hospitalized within a year of providing a patientgeneral health input, arranged in accordance with at least oneembodiment described herein. Curves 301, 303, 305, 307, and 309respectively represent the percentage of the Medicare patients that werenot hospitalized within a year of providing the patient general healthinput that indicates excellent, very good, good, fair, and poorperceived general health of the patients.

For the curve 301, corresponding to the patient general health inputthat indicates excellent perceived general health of the patients, thenumber of patients that were not hospitalized decreased as the yearprogressed. For example, one hundred percent of the patients were nothospitalized at day zero, roughly ninety five percent of the patientswere not hospitalized at roughly day one hundred ninety five, androughly eighty eight percent of the patients were not hospitalized atday three hundred sixty five.

For the curve 303, corresponding to the patient general health inputthat indicates very good perceived general health of the patients, thenumber of patients that were not hospitalized also decreased as the yearprogressed. For example, one hundred percent of the patients were nothospitalized at day zero, roughly ninety two percent of the patientswere not hospitalized at roughly day one hundred ninety five, androughly eighty three percent of the patients were not hospitalized atday three hundred sixty five.

For the curve 305, corresponding to the patient general health inputthat indicates good perceived general health of the patients, the numberof patients that were not hospitalized similarly decreased as the yearprogressed. For example, one hundred percent of the patients were nothospitalized at day zero, roughly eighty seven percent of the patientswere not hospitalized at roughly day one hundred ninety five, androughly seventy eight percent of the patients were not hospitalized atday three hundred sixty five.

For the curve 307, corresponding to the patient general health inputthat indicates fair perceived general health of the patients, the numberof patients that were not hospitalized also decreased as the yearprogressed. For example, one hundred percent of the patients were nothospitalized at day zero, roughly seventy eight percent of the patientswere not hospitalized at roughly day one hundred ninety five, androughly sixty six percent of the patients were not hospitalized at daythree hundred sixty five.

For the curve 309, corresponding to the patient general health inputthat indicates poor perceived general health of the patients, the numberof patients that were not hospitalized decreased more quickly as theyear progressed. For example, one hundred percent of the patients werenot hospitalized at day zero, roughly sixty seven percent of thepatients were not hospitalized at roughly day one hundred ninety five,and roughly fifty three percent of the patients were not hospitalized atday three hundred sixty five.

As can be seen in the graphical representation 300, a strong correlationbetween perceived general health of the patients and likelihood ofhospitalization of the patient within a year exists.

FIG. 4 is a graphical representation 400 of a number of Medicarepatients that do not die within a year of providing a patient generalhealth input, arranged in accordance with at least one embodimentdescribed herein. Curves 401, 403, 405, 407, and 409 respectivelyrepresent the percentage of the Medicare patients that did not diewithin a year of providing patient general health input that indicatesexcellent, very good, good, fair, and poor perceived general health ofthe patients.

For the curve 401, corresponding to the patient general health inputthat indicates excellent perceived general health of the patients, thenumber of patients that did not die decreased as the year progressed.For example, one hundred percent of the patients did not die by dayzero, roughly ninety nine percent of the patients did not die by roughlyday one hundred ninety five, and roughly ninety eight percent of thepatients did not die by day three hundred sixty five.

For the curve 403, corresponding to the patient general health inputthat indicates very good perceived general health of the patients, thenumber of patients that did not die also decreased as the yearprogressed. For example, one hundred percent of the patients did not dieby day zero, roughly ninety nine percent of the patients did not die byroughly day one hundred ninety five, and roughly 97.5 percent of thepatients did not die by day three hundred sixty five.

For the curve 405, corresponding to the patient general health inputthat indicates good perceived general health of the patients, the numberof patients that did not die similarly decreased as the year progressed.For example, one hundred percent of the patients did not die by dayzero, roughly ninety seven percent of the patients did not die byroughly day one hundred ninety five, and roughly 96.5 percent of thepatients did not die by day three hundred sixty five.

For the curve 407, corresponding to the patient general health inputthat indicates fair perceived general health of the patients, the numberof patients that did not die also decreased as the year progressed. Forexample, one hundred percent of the patients did not die by day zero,roughly ninety six percent of the patients did not die by roughly dayone hundred ninety five, and roughly ninety four percent of the patientsdid not die by day three hundred sixty five.

For the curve 409, corresponding to the patient general health inputthat indicates poor perceived general health of the patients, the numberof patients that did not die decreased more quickly as the yearprogressed. For example, one hundred percent of the patients did not dieby day zero, roughly eighty nine percent of the patients did not die byroughly day one hundred ninety five, and roughly eight four percent ofthe patients did not die by day three hundred sixty five.

As can be seen in the graphical representation 400, a strong correlationbetween perceived general health of the patients and likelihood of deathof the patient within a year exists.

FIG. 5 is a graphical representation 500 of how various factors mayimpact management of chronic diseases, in accordance with at least oneembodiment of the present disclosure. Example factors of the patientthat may impact management of chronic diseases may include distalfactors 530, proximal factors 532, physiological factors 534, diseasesand injuries 536, and/or health outcomes 538 of the patient.

Potential causal relationships (e.g., relationships that may have animpact on a different factor) within the factors are illustrated assolid lines in the graphical representation 500. For example, the distalfactors 530 of the patient may have a causal relationship with theproximal factors 532, the physiological factors 534, and/or the healthoutcomes 538 of the patient. As another example, the proximal factors532 of the patient may have a causal relationship with the physiologicalfactors 534 and/or the health outcomes 538 of the patient. As yetanother example, the physiological factors 534 of the patient may have acausal relationship with the diseases and injuries 536 of the patient.As another example, the diseases and injuries 536 of the patient mayhave a causal relationship with the health outcomes 538 of the patient.

Potential feedback relationships (e.g., relationships that may adjusttreatment and diagnosis of a different factor) within the factors areillustrated as dashed lines in the graphical representation 500. Forexample, the diseases and injuries 536 of the patient may have afeedback relationship with the proximal factors 532 of the patient. Asanother example, the health outcomes 538 of the patient may have afeedback relationship with the proximal factors 532 and/or the distalfactors 530 of the patient.

The distal factors 530 of the patient may include one or moresub-factors including cultural context 530 a, political context 530 b,education 530 c, poverty 530 d, and/or social connections 530 e. As canbe seen in the graphical representation 500, the sub-factors included inthe distal factors 530 may impact each other. For example, the education530 c sub-factor may be affected by the cultural context 530 a and/orthe political context 530 b sub-factors.

The proximal factors 532 of the patient may include one or moresub-factors including diet 532 a; activity level and exercise 532 b;alcohol consumption 532 c; and/or self-identity 532 d. As shown in thegraphical representation 500, the sub-factors included in the proximalfactors 532 may be impacted by one or more sub-factors in the distalfactors 530. For example, the diet 532 a sub-factor may be affected bythe education 530 c, the poverty 530 d, and/or social connectionssub-factors.

The physiological factors 534 of the patient may include one or moresub-factors including high cholesterol 534 a, blood pressure 534 b,serum cortisol 534 c, and/or blood glucose 534 d. The diseases andinjuries 536 of the patient may also include one or more sub-factorsincluding atherosclerotic cardiovascular disease (ASCVD) 536 a,diabetes, 536 b, chronic alcoholism 536 c, and/or fall with hip fracture536 d. Likewise, the health outcomes 538 of the patient may include oneor more sub-factors including function 538 a, subjective sense of health536 b, experiential state 538 c, and/or death 538 d.

Both the distal factors 530 and the proximal factors 532 may operatethrough sub-factors and directly on health outcomes 538 of the patient.For example, a level of education of the patient may directly influencetheir subjective sense of health 536 b and/or level of social function538 a and may also impact the diet 532 a and activity level and exercise532 b of the patient.

FIG. 6 is a graphical representation 600 of anatomically detailed humanavatars 611 a-b, 613, and 615, arranged in accordance with at least oneembodiment of the present disclosure. In FIG. 6 a front view of a firstavatar 611 a and a side view of the first avatar 611 b are illustrated.The first avatar 611 a-b may be provided to the patient via a userdevice such as the user device 124 of FIG. 1. The first avatar 611 a-bmay be anatomically detailed to allow the patient to provide informationspecific to where the patient is experiencing pain and/or how much painthe patient is experiencing. The information collected via the firstavatar 611 a-b may be used to determine a VAS score on a VAS pain scaleas discussed elsewhere herein.

Additionally, a side view of a second avatar 613 and a side view of athird avatar 615 illustrating different amounts of musculoskeletal painreported by a patient are illustrated. The second avatar 613 and thethird avatar 615 show amounts of reported musculoskeletal pain mapped tocorresponding musculoskeletal regions of the second avatar 613 and thethird avatar 615. In some embodiments, the second avatar 613 and/or thethird avatar 615 may include a lower left leg region 617 a-b, a lowerright leg region 619 a-b, an upper left leg region 621 a-b, an upperright leg region 637 a-b, a groin region 623 a-b, a lower right armregion 631 a-b, a lower left arm region 625 a-b, a chest region 627 a-b,an upper left arm region 629 a-b, an upper right arm region 633 a-b, anda neck region 635 a-b.

As illustrated in the second avatar 613, various amounts ofmusculoskeletal pain as reported by the patient are shown as differenthatching covering an entire region of the second avatar 613. Asillustrated in the third avatar 615, various amounts of musculoskeletalpain as reported by the patient are shown as different line types withina region of the third avatar 615.

A level of pain associated with a given hatching in the second avatar613 and/or line type in the third avatar 615 according to an exampleembodiment will now be described. As illustrated in the second avatar613 and the third avatar 615, the patient reported experiencing a highlevel of musculoskeletal pain in the lower left leg region 617 a-b andthe groin region 623 a-b. As illustrated in the second avatar 613 andthe third avatar 615, the patient reported experiencing a very highlevel of musculoskeletal pain in the lower right leg region 619 a-b.Additionally, as illustrated in the second avatar 613 and the thirdavatar 615, the patient reported experiencing an intermediate level ofmusculoskeletal pain in the chest region 627 a-b, the lower right armregion 631 a-b, and the lower right arm region 631 a-b. Furthermore, asillustrated in the second avatar 613 and the third avatar 615, thepatient reported experiencing a low level of musculoskeletal pain in theupper right arm region 633 a-b, the upper left arm region 629 a-b, andthe neck region 635 a-b. As illustrated in the second avatar 613 and thethird avatar 615, the patient reported experiencing no musculoskeletalpain in the upper right leg region 637 a-b and the upper left leg region621 a-b.

FIG. 7 is a flow diagram of an example method 700 to predict alikelihood of a patient experiencing an acute event (e.g., visiting anER and/or hospitalization) in the near future, arranged in accordancewith at least one embodiment described herein. The method 700 may beperformed by a graded escalation module such as the graded escalationmodule 110 of FIG. 1. In some embodiments, the graded escalation modulemay have access to a chronic disease database, such as the chronicdisease database 126 of FIG. 1 and/or an EHR database such as the EHRdatabase 128 of FIG. 1. Additionally, the graded escalation module mayhave access to one or more sensors, such as the sensors 120 and 122 ofFIG. 1.

The method 700 may be performed, in whole or in part, by the gradedescalation module. Alternatively or additionally, the method 700 may beimplemented by a processor device that performs or controls performanceof one or more of the operations of the method 700. For instance, acomputer (such as the computing device 1400 of FIG. 14) or otherprocessor device may be communicatively coupled to the graded escalationmodule and/or may be included as a control system of the gradedescalation module and may execute software or other computer-readableinstructions accessible to the computer, e.g., stored on anon-transitory computer-readable medium accessible to the computer, toperform or control the graded escalation module to perform the method700 of FIG. 7.

The method 700 may include one or more of blocks 702, 704, 706, 708,710, 712, and/or 714. Although illustrated as discrete blocks, variousblocks may be divided into additional blocks, supplemented withadditional blocks, combined into fewer blocks, or eliminated, dependingon the particular implementation. The method 700 may begin at block 702.

In block 702 (“Collect Sensor Data Related To A Quality Of Health Of APatient”), sensor data related to a quality of health of a patient maybe collected. In some embodiments, the sensor data may be collected bythe graded escalation module from the sensors. Block 702 may be followedby block 704.

In block 704 (“Receive Input From The Patient That Provides AQuantification Of A Present Health Of The Patient”), input from thepatient that provides a quantification of the present health of thepatient may be received. The input from the patient may provide aquantification of the present health of the patient using a VAS basedhealth status scale (e.g., a VAS pain scale). In some embodiments, theVAS based health status scale may be obtained by the patient indicatingon an avatar a location of pain, an amount of pain, and/or a type ofpain that the patient is experiencing. The avatar may include one ormore of the avatars 611 a-b, 613, and/or 615 discussed elsewhere herein.In some embodiments, the patient input may provide a quantification oflifestyle choices of the patient. Block 704 may be followed by block706.

In block 706 (“Generate A First Quality Of Health Marker”), a firstquality of health marker may be generated. The first quality of healthmarker may be generated based on the sensor data related to the qualityof health of the patient and the quantification of the present health ofthe patient. In some embodiments, the first quality of health marker maybe generated based on at least one of the sensor data related to thequality of health of the patient and the patient input. In these andother embodiments, the first quality of health marker may include afirst dimension of the quality of health of the patient. Additionally oralternatively, the first quality of health marker may be indicative ofadditional examination of the quality of health of the patient to beperformed.

In some embodiments, the first quality of health marker may include afirst dimension of the quality of health of the patient. The firstquality of health marker may indicate whether additional examination ofthe quality of health of the patient is to be performed. Block 706 maybe followed by block 708.

In block 708 (“Generate A Second Quality Of Health Marker”) a secondquality of health marker may be generated. In some embodiments, thesecond quality of health marker may be based on at least one of thesensor data related to the quality of health of the patient and thepatient input. In these and other embodiments, the second quality ofhealth marker may be a second dimension of the quality of health of thepatient. Additionally or alternatively, the second quality of healthmarker may be indicative of additional examination of the quality ofhealth of the patient to be performed.

The second quality of health marker may be generated based on one ormore risk stratification algorithms. In some embodiments, the secondquality of health marker may indicate an acute assessment of issuesrelated to the quality of health of the patient. In some embodiments,the method 700 may also include generating a third quality of healthmarker based on one or more risk stratification algorithms. In someembodiments, the third quality of health marker may be a third dimensionof the quality of health of the patient. Additionally or alternatively,the third quality of health marker may be indicative of an acuteassessment of issues related to the quality of health of the patient.

Additionally, the method 700 may include generating a HICO score basedon the first quality of health marker and the second quality of healthmarker, as discussed in more detail below. Likewise, the method 700 mayinclude generating a BICO score of the patient using lifestyle choicesbased on at least one of the patient input and the sensor data, asdiscussed in more detail below. Block 708 may be followed by block 710.

In block 710 (“Compare At Least One Of The First Quality Of HealthMarker To A First Quality Of Health Marker Baseline Value And The SecondQuality Of Health Marker To A Second Quality Of Health Marker BaselineValue”), at least one of the first quality of health marker may becompared to the first quality of health marker baseline value and thesecond quality of health marker may be compared to the second quality ofhealth marker baseline value. In some embodiments, the method 700 mayalso include comparing the HICO score and the BICO score with a set ofbaseline values. Block 710 may be followed by block 712.

In block 712 (“Generate A Quality Of Health Result”), a quality ofhealth result may be generated. The quality of health result may bebased on the comparison of at least one of the first quality of healthmarker to the first quality of health marker baseline value and thesecond quality of health marker to the second quality of health markerbaseline value. The quality of health result may indicate the likelihoodof a patient experiencing an acute event in the near future. Block 712may be followed by block 714.

In block 714 (“Provide An Alert To A Care Provider”), an alert may beprovided to the care-provider. In some embodiments, the care-providermay include a physician of the patient. The alert may include thequality of health result. In some embodiments, the alert is providedonly if the quality of health result is below a quality of healththreshold amount (e.g., the first quality of health marker is below afirst quality of health marker threshold value and/or the second qualityof health marker is below a second quality of health marker thresholdvalue), or if the quality of health result has deteriorated from a priorquality of health result by a difference more than a quality of healththreshold difference (e.g., an iteration threshold value). In otherembodiments, the alert may be provided for each and every quality ofhealth result.

In some embodiments, the alert may include the result of at least one ofthe comparison of the first quality of health marker to the firstquality of health marker baseline value, the second quality of healthmarker to the second quality of health marker baseline value, the HICOscore and the BICO score to the set of baseline values.

FIG. 8 is a flow diagram of an example method 800 to evaluate relativerisk of under-diagnosis of a patient, arranged in accordance with atleast one embodiment described herein. The method 800 may be performedby an under diagnosis module such as the under diagnosis module 112 ofFIG. 1. In some embodiments, the under diagnosis module may have accessto a chronic disease database, such as the chronic disease database 126of FIG. 1 and/or an EHR database such as the EHR database 128 of FIG. 1.

The method 800 may be performed, in whole or in part, by the underdiagnosis module. Alternatively or additionally, the method 800 may beimplemented by a processor device that performs or controls performanceof one or more of the operations of the method 800. For instance, acomputer (such as the computing device 1400 of FIG. 14) or otherprocessor device may be communicatively coupled to the under diagnosismodule and/or may be included as a control system of the under diagnosismodule and may execute software or other computer-readable instructionsaccessible to the computer, e.g., stored on a non-transitorycomputer-readable medium accessible to the computer, to perform orcontrol the under diagnosis module to perform the method 800 of FIG. 8.

The method 800 may include one or more of blocks 802, 804, 806, 808,and/or 810. Although illustrated as discrete blocks, various blocks maybe divided into additional blocks, supplemented with additional blocks,combined into fewer blocks, or eliminated, depending on the particularimplementation. The method 800 may begin at block 802.

In block 802 (“Collect Data That Indicates A Number Of Patients That AreDiagnosed With One Or More Chronic Diseases In A Geographic Location”),data that indicates a number of patients that are diagnosed with one ormore chronic diseases in a geographic location may be collected. Thedata may be obtained from a chronic disease database such as the chronicdisease database 126 of FIG. 1. The data may include the chronic datadiscussed elsewhere herein. Block 802 may be followed by block 804.

In block 804 (“Collect Data That Indicates A Number Of Patients That AreDiagnosed With The One Or More Chronic Diseases In A Practice Of APhysician”), data that indicates a number of patients diagnosed with theone or more chronic diseases in a practice of a physician may becollected. The data may be obtained from an EHR database such as the EHRdatabase 128 of FIG. 1. The data may include the EHR data discussedelsewhere herein. Block 804 may be followed by block 806.

In block 806 (“Compare The Number Of The Patients Diagnosed With The OneOr More Chronic Diseases In The Practice Of The Physician To The NumberOf Patients Diagnosed With The One Or More Chronic Diseases In TheGeographic Location”), the number of the patients diagnosed with the oneor more chronic diseases in the practice of the physician may becompared to the number of patients diagnosed with the one or morechronic diseases in the geographic location. Block 806 may be followedby block 808.

In block 808 (“Generate A Comparison Result”) a comparison result may begenerated. The comparison result may indicate whether the number ofpatients diagnosed with the one or more chronic diseases in the practiceof the physician is greater than or less than the number of patientsdiagnosed with the one or more chronic diseases in the geographiclocation. Alternatively or additionally, the comparison result mayinclude a difference between the number of patients diagnosed with theone or more chronic diseases in the practice of the physician and thenumber of patients diagnosed with the one or more chronic diseases inthe geographic location. Block 808 may be followed by block 810.

In block 810 (“Provide An Alert To A Care Provider”), an alert may beprovided to the care-provider. The alert may include the comparisonresult. In some embodiments, the care-provider may include a physicianof the patient. In some embodiments, the alert is provided only if thecomparison result indicates the number of patients diagnosed with theone or more chronic diseases in the practice of the physician is lessthan the number of patients diagnosed with the one or more chronicdiseases in the geographic location and/or if the number of patients ata higher risk for the one or more chronic diseases in the practice ofthe physician is less than the number of patients at a higher risk forthe chronic disease in the geographic location. In other embodiments,the alert may be provided for each and every comparison result.

FIG. 9 is a flow diagram of an example method 900 to evaluate andstratify a chronic care burden of a patient, arranged in accordance withat least one embodiment described herein. The method 900 may beperformed by a chronic burden module such as the chronic burden module108 of FIG. 1. In some embodiments, the chronic burden module may haveaccess to a chronic disease database, such as the chronic diseasedatabase 126 of FIG. 1 and/or an EHR database such as the EHR database128 of FIG. 1. Additionally, the chronic burden module may have accessto one or more sensors, such as the sensors 120 and 122 of FIG. 1.

The method 900 may be performed, in whole or in part, by the chronicburden module. Alternatively or additionally, the method 900 may beimplemented by a processor device that performs or controls performanceof one or more of the operations of the method 900. For instance, acomputer (such as the computing device 1400 of FIG. 14) or otherprocessor device may be communicatively coupled to the chronic burdenmodule and/or may be included as a control system of the chronic burdenmodule and may execute software or other computer-readable instructionsaccessible to the computer, e.g., stored on a non-transitorycomputer-readable medium accessible to the computer, to perform orcontrol the chronic burden module to perform the method 900 of FIG. 9.

The method 900 may include one or more of blocks 902, 904, 906, 908,910, 912, and/or 914. Although illustrated as discrete blocks, variousblocks may be divided into additional blocks, supplemented withadditional blocks, combined into fewer blocks, or eliminated, dependingon the particular implementation. The method 900 may begin at block 902.

In block 902 (“Collect Sensor Data Related To A Quality Of Health Of APatient”), sensor data related to a quality of health of a patient maybe collected. In some embodiments, the sensor data may be collected bythe chronic burden module from the sensors. In these and otherembodiments, the sensor data may be related to a physical or mentalhealth of the patient. Alternatively or additionally, the sensor datamay pertain to, include, and/or be indicative of at least one of a dietpattern, a sleep pattern, an exercise pattern, an activity level, heartrate, posture, stress, blood pressure variation, blood glucose, heartrhythm, smoking status, pain level, and/or GPS data of the patient.Block 902 may be followed by block 904.

In block 904 (“Collect Patient Data”), patient data may be collected.The patient data may indicate at least one of a biological profile ofthe patient, a psychological profile of the patient, a social profile ofthe patient, physician notes related to the biological profile of thepatient, physician notes related to the psychological profile of thepatient, a baseline of sensor based data, and physician notes related tothe social profile of the patient. Block 904 may be followed by block906.

In block 906 (“Receive Input From the Patient”), patient input may bereceived from the patient. Alternatively or additionally, block 906 mayinclude receiving one or both of manual inputs from the patient and/orsensor outputs from one or more sensors collecting data from thepatient. The patient input may provide a quantification of a presenthealth risk assessment of the patient using minimum clicks in minimumcategories using a VAS. In some embodiments, the VAS may be obtained bythe patient indicating on an avatar a location of pain, an amount ofpain, and/or a type of pain that the patient is experiencing. The avatarmay include one or more of the avatars 611 a-b, 613, and/or 615discussed elsewhere herein. Block 906 may be followed by block 908.

In block 908 (“Receive Patient Input In Response To One Or MoreQuestions Directed To A Quality Of Health Of The Patient”) patient inputin response to one or more questions directed to a quality of health ofthe patient may be received. In some embodiments, the patient input maybe received in response to one or more questionnaires being provided tothe patient by a questionnaire module such as the questionnaire module104 of FIG. 1. In these and other embodiments, the patient input may bereceived in response to one or more questionnaires being provided to thepatient by a disease calculator, such as the disease calculator 106 ofFIG. 1. Block 908 may be followed by block 910.

In block 910 (“Generate A Chronic Care Burden (CCB) Score With APre-Defined Risk Stratification”), a CCB score with a pre-defined riskstratification may be generated. The CCB score with the pre-defined riskstratification may be generated based on at least two of the patientdata, the sensor data, and the patient input. Block 910 may be followedby block 912.

In block 912 (“Generate A CCB Result”), a CCB result may be generated.The CCB result may include the CCB score and a risk stratification ofthe patient based on the pre-defined risk stratification and the CCBscore. Block 912 may be followed by block 914.

In block 914 (“Provide The CCB Result To A Care Provider”), the CCBresult may be provided to the care-provider. In some embodiments, thecare-provider may include a physician of the patient. In someembodiments, the CCB result is provided to the care-provider only if theCCB result exceeds a CCB threshold amount and/or if the CCB result hasdeteriorated from a prior CCB result by an amount more than a CCBdifference threshold. In other embodiments, the alert may be providedfor each and every CCB result.

FIG. 10 is a flow diagram of an example method 1000 to generate a healthrisk score of a patient, arranged in accordance with at least oneembodiment described herein. The method 1000 may be performed by a totalhealth module such as the total health module 116 of FIG. 1. In someembodiments, the total health module may have access to a chronicdisease database, such as the chronic disease database 126 of FIG. 1and/or an EHR database such as the EHR database 128 of FIG. 1.Additionally, the total health module may have access to one or moresensors, such as the sensors 120 and 122 of FIG. 1.

The method 1000 may be performed, in whole or in part, by the totalhealth module. Alternatively or additionally, the method 1000 may beimplemented by a processor device that performs or controls performanceof one or more of the operations of the method 1000. For instance, acomputer (such as the computing device 1400 of FIG. 14) or otherprocessor device may be communicatively coupled to the total healthmodule and/or may be included as a control system of the total healthmodule and may execute software or other computer-readable instructionsaccessible to the computer, e.g., stored on a non-transitorycomputer-readable medium accessible to the computer, to perform orcontrol the total health module to perform the method 1000 of FIG. 10.

The method 1000 may include one or more of blocks 1002, 1004, 1006,1008, 1010, 1012, 1014, and/or 1016. Although illustrated as discreteblocks, various blocks may be divided into additional blocks,supplemented with additional blocks, combined into fewer blocks, oreliminated, depending on the particular implementation. The method 1000may begin at block 1002.

In block 1002 (“Collect Sensor Data Related To A Quality Of Health Of APatient”), sensor data related to a quality of health of a patient maybe collected. In some embodiments, the sensor data may be collected bythe total health module from the sensors. In these and otherembodiments, the sensor data may pertain to, include, and/or indicate atleast one of a diet pattern, a sleep pattern, an exercise pattern, anactivity level, heart rate, posture, stress, blood pressure variation,blood glucose, heart rhythm, smoking status, pain level, and/or GPS dataof the patient. Block 1002 may be followed by block 1004.

In block 1004 (“Receive Patient Input In Response To A Minimum Number OfQuestions For High Statistical Measurement Of Three Health Dimensions OfThe Patient”), patient input in response to a minimum number ofquestions for high statistical measurement of three health dimensions ofthe patient may be received. Block 1004 may be followed by block 1006.

In block 1006 (“Generate A Health Score For Each Of The Three HealthDimensions Of The Patient Including Acute Health, Chronic Health, andLifestyle Health Of The Patient”), a health score for each of the threehealth dimensions of the patient including acute health, chronic health,and lifestyle health of the patient may be generated. The health scorefor a first health dimension may be based on an SHC score, the healthscore for a second health dimension may be based on a CCB score, and thehealth score for a third health dimension may be based on a LCC score.The SHC score may be the same or similar to the SHC score generated bythe graded escalation module 110 of FIG. 1. The CCB score may be thesame or similar to the CCB score generated by the chronic burden module108 of FIG. 1. The LCC score may be the same or similar to the LCC scoregenerated by the lifestyle choice module 114 of FIG. 1. Block 1006 maybe followed by block 1008.

In block 1008 (“Categorize The Health Score For Each Of The Three HealthDimensions”) the health score for each of the three health dimensionsmay be categorized. Block 1008 may be followed by block 1010.

In block 1010 (“Determine The Minimum Category Of Questions For HighStatistical Measurement Of Each Category Of The Three HealthDimensions”), the minimum category of questions for high statisticalmeasurement of each category of the three health dimensions may bedetermined. Block 1010 may be followed by block 1012.

In block 1012 (“Quantify Each Of The Three Health Dimensions Of ThePatient Per A Visual Analog Scale (VAS) Health Score”), each of thethree health dimensions of the patient per a VAS health score may bequantified. Block 1012 may be followed by block 1014.

In block 1014 (“Generate A Health Risk Score”), a health risk score maybe generated. The health risk score may be based on the health score foreach of the three health dimensions. The health risk score may be thesame or similar to the health risk score discussed elsewhere herein.Block 1014 may be followed by block 1016.

In block 1016 (“Provide A Health Risk Result To A Care-Provider”), ahealth risk result may be provided to the care-provider. The health riskresult may include the health risk score. In some embodiments, thecare-provider may include a physician of the patient. In someembodiments, the health risk result is provided only if a trend of thehealth of the patient is improving (e.g., a second health risk resultincludes a higher score than a first health risk result). In otherembodiments, the health risk result is provided only if a trend of thehealth of the patient is declining (e.g., the second health risk resultincludes a lower score than the first health risk result). In yet otherembodiments, the alert may be provided for each and every comparisonresult.

FIG. 11 is a flow diagram of an example method 1100 to evaluate andstratify a lifestyle health compliance of a patient, arranged inaccordance with at least one embodiment described herein. The method1100 may be performed by a lifestyle choice module such as the lifestylechoice module 114 of FIG. 1. In some embodiments, the lifestyle choicemodule may have access to a chronic disease database, such as thechronic disease database 126 of FIG. 1 and/or an EHR database such asthe EHR database 128 of FIG. 1. Additionally, the lifestyle choicemodule may have access to one or more sensors, such as the sensors 120and 122 of FIG. 1.

The method 1100 may be performed, in whole or in part, by the lifestylechoice module. Alternatively or additionally, the method 1100 may beimplemented by a processor device that performs or controls performanceof one or more of the operations of the method 1100. For instance, acomputer (such as the computing device 1400 of FIG. 14) or otherprocessor device may be communicatively coupled to the lifestyle choicemodule and/or may be included as a control system of the lifestylechoice module and may execute software or other computer-readableinstructions accessible to the computer, e.g., stored on anon-transitory computer-readable medium accessible to the computer, toperform or control the lifestyle choice module to perform the method1100 of FIG. 11.

The method 1100 may include one or more of blocks 1102, 1104, 1106,1108, 1110, and/or 1112. Although illustrated as discrete blocks,various blocks may be divided into additional blocks, supplemented withadditional blocks, combined into fewer blocks, or eliminated, dependingon the particular implementation. The method 1100 may begin at block1102.

In block 1102 (“Collect Sensor Data Related To A Set Of LifestyleActivities Related To One Or More Chronic Diseases Of A Patient”),sensor data related to a set of lifestyle activities related to one ormore chronic diseases of a patient may be collected. In someembodiments, the sensor data may be collected by the lifestyle choicemodule from the sensors. In these and other embodiments, the sensor datamay pertain to, include, and/or indicate at least one of a diet pattern,a sleep pattern, an activity level, heart rate, posture, stress, bloodpressure variation, blood glucose, heart rhythm, smoking status, painlevel, and/or GPS data of the patient. Block 1102 may be followed byblock 1104.

In block 1104 (“Collect Patient Data”), patient data may be collected.The patient data may indicate at least one of a biological profile ofthe patient, a psychological profile of the patient, a social profile ofthe patient, and/or one or more lifestyle choice prescriptions of thepatient. Block 1104 may be followed by block 1106.

In block 1106 (“Receive Patient Input”), patient input may be receivedfrom the patient. The patient input may indicate compliance of thepatient with the lifestyle choice prescriptions. Block 1106 may befollowed by block 1108.

In block 1108 (“Determine A Lifestyle Choice Compliance (LCC) Score OfThe Patient”) a LCC score may be determined. The LCC score may be basedon at least one of the sensor data related to the set of lifestyleactivities and the patient input that indicates compliance of thepatient with the lifestyle choice prescriptions. Block 1108 may befollowed by block 1110.

In block 1110 (“Generate A LCC Result”) a LCC result may be generated.The LCC result may include the LCC score. Block 1110 may be followed byblock 1112.

In block 1112 (“Provide The LCC Result To A Care Provider”), the LCCresult may be provided to the care-provider. In some embodiments, thecare-provider may include a physician of the patient. In someembodiments, the LCC result is provided to the care-provider only if theLCC score of the patient falls below a pre-specified limit. In otherembodiments, the alert may be provided for each and every LCC result.

The data for Health Score can be collected from the patients enrolled inthe care program or collected from public domain. It can be categorizedin many ways with one embodiment being Gender, Age and State. For eachcategory, distributions are created for general health, physical health,mental health and activity limitation. As an example of just one out ofmany similar useful embodiments, the following hazard ratios may beeffective:

General Health:

-   Value: 1; Hazard ratio: 1-   Value: 2; Hazard ratio: 12.3-   Value: 3; Hazard ratio: 21.0-   Value: 4; Hazard ratio: 44.5-   Value: 5; Hazard ratio: 110.4

Physical Health:

-   Value: 0; Hazard ratio: 1-   Value: 1 to 10; Hazard ratio: 13.0-   Value: 11 to 20; Hazard ratio: 21.3-   Value: 21 to 30; Hazard ratio: 32.0

Mental Health:

-   Value: 0; Hazard ratio: 1-   Value: 1 to 10; Hazard ratio: 12.3-   Value: 11 to 20; Hazard ratio: 19.7-   Value: 21 to 30; Hazard ratio: 23.6

Activity Limitation:

-   Value: 0; Hazard ratio: 1-   Value: 1 to 10; Hazard ratio: 14.9-   Value: 11 to 20; Hazard ratio: 23.4-   Value: 21 to 30; Hazard ratio: 36.5

Now, the raw HRQoL score may be created by combining the percentilescores after weighting them suitably with the hazard ratios. As anexample of just one, out of many, such useful embodiments, one can usethe formulae:

HRQoL score=(General health percentile)*(1−general health hazardratio/K)+(Physical health percentile)*(1−physical health hazardratio/K)+(Mental health percentile)*(1−mental health hazardratio/K)+(Activity limitation percentile)*(1−Activity limitation hazardratio/K);

-   -   where “K” can be derived using the normalized hazards from the        General health category.

The normalized HRQoL score may be calculated in several ways with oneembodiment being based on the range observed in the population analyzed.For example, one can use the following formulae:

Normalized HRQoL score=(HRQoL score−0.199897)/(3.752099−0.199897).

Now, using the Activity Score as computed in equation 1, a comprehensivehealth score (HICO) for the given patient can be calculated by suitablycombining the normalized HRQoL score with the activity score. As anexample of just one of our many similar useful embodiments, one can usethe following formula: HICO=0.5*Normalized HRQoL score+0.5*ActivityScore.

It will be clear to a person skilled in the state of the art that anHRQoL score can also be calculated based on the statistical distributionof the sensor output values obtained from a representative sample wherethe sensor output values can represent either markers for physiologicalattributes such as heart rate, heart rhythm, blood pressure, bloodglucose, weight, diet, chore related activity, exercise, smokingstatistics, pollution encountered, etc. OR quality of life relatedattributes such as stress, mood, sleep duration, sleep quality, posture,social interaction, home bound time, leisure related activities, etc.The quantification of quality of life related attributes may bemeasured, for example, by the deviation of each such attribute from therepresentative sample and the physiological attributes may be measuredby (preferably) deviation from the physician preferred values or thecomparable population statistics. For example, the average bloodpressure in the given sample may be 140/95 though the desired bloodpressure, per the physician, may be 120/80, and the comparison can bemade with respect to either of the reference values. Each such attributepertaining to quality of life can then be combined based upon thepercentile scores of that attribute. The weight of percentile value ofeach attribute can be assigned based on the preferred definition ofHRQoL or may be chosen either by the care provider or by the patient orset a priori in consultation with the patient. For example, for apatient who is deemed by his care-provider as naturally introvert bychoice, stress, mood, sleep can assume far greater weightage than aperson who is deemed by his care provider as naturally extrovert bychoice. For the person, naturally extrovert by choice, increase insocial interactions or leisure related activities or reduction in homebound time will imply a greater improvement in quality of life. TheHRQoL value obtained in this manner can be then weighted using thephysiological attributes so that quality of life related attributesmeasurement can be normalized with respect to the health of the patienttoo. For example, when two patients have exactly equal attributes withrespect to the quality of life but one of them maintains a bettercontrol on blood pressure, heart rate, and blood glucose then hisHRQoLis deemed better.

In some embodiments, a physical predictive model of the physical healthof a patient (herein ‘physical predictive model’) may be determined. Thephysical predictive model may be the same or similar to the health riskscore. For example, the physical predictive model of the physical healthof the patient may indicate an overall physical health of the patient.The physical predictive model may be determined by combining big data(e.g., CDC data) with patient data based on one or more of the followingobservations: a breakdown in health of a patient may be preceded by anacute episode which may occur when a disease burden is initiallydetected as a symptomatic occurrence; initial system-wide symptomaticdisturbances of the patient may be tilted back to an originalsymptom-less homeostatic balance; and/or after experiencing moderatesymptomatic changes, the patient is likely to experience increasedtiredness and/or decreased resilience when experiencing symptoms, whichmay increase an amount of time for patient recovery post symptom whichmay take longer than patient recovery from activity pre symptom due tothe onset of disease based reduced resilience.

In some embodiments, the physical predictive model may be determineddaily for a period of time. In these and other embodiments, a day mayinclude a period of time between a start node that relates to thepatient waking up and an end node that relates to the patient fallingasleep. Additionally, each day may include a number of states of thepatient (e.g., ADL bins and/or activity bins). The period of time mayinclude a week, a month, or any other appropriate period of time. Insome embodiments, the resilience time may indicate how much time haselapsed between an activity (e.g., exercise) and biomarkers of thepatient returning to normal. The physical predictive model mayadditionally include resilience time of similar activities.

In some embodiments, the patient recovery pre symptom and/or a tirednesslevel of the patient post symptom may be determined using sensors (e.g.,smartphones, wearable, and/or non-wearable sensors). If either thepatient recovery from activity pre symptom and/or the tiredness level ofthe patient post symptom decreases, a similar decrease in the overallphysical health of the patient may occur.

The physical predictive model may be determined based on the comparisonof the big data with patient data based on the observations listedabove. In some embodiments, a hidden Markov model may be used todetermine the physical predictive model. The physical predictive modelmay be generated using one or more nodes that represent different statesof the patient and edges which connect such nodes. The edges refer to amoving average of previous instances of patient resilience duringperiods of time.

In some embodiments, a day may include a series of states (e.g., nodes)of the patient. The states may correspond to the current day and thecurrent health of the patient. For example, a node may correspond to thepatient being healthy on a first day of the period of time.Additionally, the states of the patient may correspond to specificdiseases, specific habits, and/or specific risks of the patient.Furthermore, the states of the patient may correspond to specificdiseases, specific habits, and/or specific risks mapped to differentweather types, seasons of the year, days of the week. For example, athird state of the patient may correspond to a heat stroke state of thepatient on the first day in which an average temperature for the day isabove eighty five degrees Fahrenheit.

In some embodiments, the states of the patient may be based on portionsof data included in the EHR data, such as BMI, blood pressure control,smoking status, depression status, or any other appropriate data pointof the patient. Additionally, the states of the patient may be based oninsurance data, for example, Medicare data, related to risk scores ofthe patient and/or expenses incurred due to chronic diseases of thepatient. Additionally, the states of the patient may be based on dataincluded in the patient HRQOL input received from the patient. Forexample, the states of the patient may be based on the number of healthydays versus the number of unhealthy days physically and/or mentally thatthe patient reports experiencing.

The states of the patient may represent numerical values for a dayaccording to one or more metrics. For example, the metrics may includean average activity level in a day, an average tiredness level in a day,or any other appropriate metric of the patient. The states of thepatient may be weighted based on both a physical aspect and anautonomous resiliency aspect of the patient. In some embodiments, thestates of the patient to be considered in determining the physicalpredictive model may be determined by combining the short term healthscore, the chronic care burden score, and/or the lifestyle choice score.

A probability of the patient being in a particular state (e.g., POO) maybe determined using evidence nodes. The evidence nodes may be based on alevel of compliance of the patient with one or more goals and/orschedule for the patient on a day. For example, a first evidence nodemay be based on compliance of the patient with a specific goal on thefirst day and a second evidence node may be based on compliance of thepatient with a specific schedule on a second day. In some embodiments,the evidence nodes may be associated with a total activity level, atotal sedentary time, a total sleep, a blood pressure, a blood glucose,a pulmonary, and/or a pain level of the patient.

Each evidence node may include values from the different states of thepatient with a corresponding probability. For example, an evidence nodemay include values from a healthy node (e.g., a healthy state of thepatient) with probability PH and from an unhealthy node with probabilityPu. In some embodiments, the value of the evidence nodes may alsorepresent the resiliency, an exercise schedule, and/or deviation fromthe schedule of the patient. Thus, different evidence nodes may includedifferent distributions from which values of the evidence nodes aresampled. The evidence node values may be used to determine theprobability of the patient transitioning from one state to anotherstate. For example, the probability of the patient transitioning from ahealthy state to an unhealthy state.

The value of each evidence node may include a distribution specific tothe patient. The distribution specific to the patient may be determinedbased on an initial physical training set of data. Additionally, thedistribution specific to the patient may be extrapolated to similarpatients with similar body build and/or types. In some embodiments, thedistribution specific to the patient may be determined using empiricalevidence, which may indicate a physical baseline of the patient for eachevidence node (e.g., observed deviations from an expected mean of anevidence node for each state). For example, the resilience time may becompared to a resilience baseline of the patient. The comparison may beused to predict how the activity level of the patient deviated from theexpected ADLs and/or activity bins and/or to predict what the physicalhealth of the patient is on a day.

In some embodiments, the physical training set of data may include datarelated to each numerical combination of physical health scores of thepatient. For example, the training set of data may include data relatedto all possible scores for combining the short term health score, thelifestyle choice score, and/or the chronic burden score of the patient.A distribution of sensor data may be determined for specific values ofthe short term health score, the lifestyle choice score, and/or thechronic burden score, which may permit the physical predictive model tobe determined.

In some embodiments, an expected number of evidence nodes may bedetermined per day and/or per period of time. The evidence node valuesmay represent the level of compliance with the one or more goals and/orthe schedule. For example, an evidence node may include a score of onefor complete compliance and a score of zero for no compliance with thegoals and/or the schedule. An evidence node may include a score betweenzero and one hundred for varying levels of compliance with the goalsand/or schedule. In some embodiments, the value of each evidence nodemay be weighted based on a statistical significance of the correspondingevidence node.

A range of values for the evidence nodes may be assigned with aprobability based on the physical baseline of the patient. For example,an event in which the heart rate of the patient takes three to fourminutes to return to resting rate may be labelled as event A and anevent in which the heart rate of the patient takes four to five minutesto return to resting rate may be labelled as event B. Empiricallydetermined probabilities for event A and event B may be determined andassigned.

At the end of each day, resiliency levels of the patient and/ordeviation from the expected number of evidence nodes may be determined.Additionally, the physical predictive model of the physical health ofthe patient may be determined using percentile based computation inwhich a mean-centered binomial distribution of population based metricsare centered at a score of fifty and scores associated with healthypatients are distributed towards one hundred.

A probability of the patient transitioning from a current state (e.g.,π_(t)) on one day to another state (e.g., π_(t)′) on a subsequent daymay be determined (e.g., P(π_(t)|π_(t)′)). A probability of the patientbeing in one state may be determined using Equation 2.

P(π_(t) |E _(day 1) ,E _(day 2) , . . . , E _(day n)))  Equation 2

In Equation 2, E_(day 1) through E_(day n) may represent the evidencenodes for each day of the period of time. E_(day n) may correspond to afinal day in the period of time. For example, in an embodiment in whichthe period of time is one week, E_(day n) may be E_(day 7). As anotherexample, in an embodiment in which the period of time is one week,E_(day 4) may represent the evidence node associated with Thursday sinceThursday is the fourth day of a week. Additionally, each state of thepatient may only be associated with one output evidence node. Forexample, a state corresponding to a specific state of the patient onThursday (e.g., day 4) may be associated with a single evidence nodethat corresponds to E_(day 4).

In an example in which the patient may be in one of two states (e.g., ahealthy state or a sick state) on a day, the probability of the patientbeing in the sick state may be determined using conditional probability.The probability of the patient being in a sick state given N evidencenodes may be determined according to Equation 3.

P(π_(t)=Sick|E _(Day1) ,E _(Day2) , . . . , E _(DayN))=[P _(S)*(Π^(n)_(i=1) P(E _(Day(i)) |E _(Day(i−1)) , E _(Day(i−2)) , . . . , E_(Day1),Sick))]/P(E _(Day1) , . . . , E _(DayN))=[P _(S)*(Π^(n) _(i=1)P(E _(Day(i)) |E _(Day(i+1)) , E _(Day(i+2)) , . . . , E_(DayN),Sick))]/P(E _(Day1) , . . . , E _(DayN))  Equation 3

In Equation 3, P_(S) may represent the probability that the patient isin the sick state.

In another example in which the patient may be in one of three states(e.g., the healthy state, an intermediate state, or the sick state) on aday, the probability of the patient being in one of the three statesgiven N evidence nodes may initially be determined according to Equation2. The probability of the patient being in one of the three states maybe marginalized over all three states, since the evidence nodes mayinclude conditional independence according to Equation 4:

E _(Day i)⊥{π_(x) :x≠i}  Equation 4

In Equation 4, x may represent day number in a series of days. Thesignificance level of the current state may be determined recursivelyusing Equations 5, 6, and 7.

α_(t)(π_(t))=P(π_(t) ,E _(day 1:t))=Σ_(πt−1) P(π_(t),π_(t−1) , E_(day 1:t))  Equation 5

α_(t)(π_(t))=P(E _(day t)|π_(t))Σ_(π) _(t−1) P(E _(Day t)|π_(t),π_(t−1),E _(day 1:t−1))P(π_(t)|π_(t−1) ,E _(day 1:t−1))P(π_(t−1) ,E_(day 1:t−1))   Equation 6

α_(t)(π_(t))=P(E _(day t)|π_(t))Σ_(π) _(t−1)P(π_(t)|π_(t−1))α_(t−1)(π_(t−1))  Equation 7

P(E_(day t)|π_(t)) and P(π_(t)|π_(t−1)) may be given as transitionprobabilities in the physical predictive model. Based on Equations 5, 6,and 7, the probability of the patient being in the current state on aday given the evidence node values may be determined according toEquation 8.

$\begin{matrix}{{P( {\pi_{t}E_{{day}\mspace{14mu} 1\text{:}t}} )} = \frac{\alpha_{t{(\pi_{t})}}}{\Sigma_{i \in \Omega}\mspace{14mu} {\alpha_{i}( \pi_{i} )}}} & {{Equation}\mspace{14mu} 8}\end{matrix}$

In Equation 8, Ω may represent a set of states the patient may be in onthe day.

An example program in which the probability of the patient being healthyor sick, given the evidence nodes over that last N days; thedistribution of the states of the patient; the probability oftransitioning between each state; the physical baselines of the patient;and a model setup may include:

function calculate_current_state(n_observations, day): markov_model =new Model(transition_probabilities, distribution_per_node)possible_states = markov_model.get_possible_states(day)today_observation = getTodaysData( ) state_probabilities = [ ] for statein possible_states:prob=markov_model.get_probability_of_state(today_observation,n_observations)state_probabilities += [(state, prob)] return(argmax(state_probabilities), state_probabilities)In the example program, the function get_probability_of_state( ) maydetermine (Pπ_(t)|E_(Day1), E_(Day2), . . . , E_(DayN)) for eachpossible state of the patient. Additionally, the functionget_probability_of_state( ) may return a tuple of the most likely stateusing distribution over all states.

In some embodiments, a metric of vitality may be determined using amoving average of the total activity and the tiredness levels of thepatient. For example, K evidence nodes may represent a tuple of high ormoderate activity in a day and the tiredness level of the patient. Thevalues of the K evidence nodes may be adjusted based on a sequence ofactivities. For example, the value associated with the tiredness level(e.g., how tired the patient should be at the end of the day) may beadjusted by an amount if that day includes a high intensity activity(Activity1) followed by a moderate intensity activity (Activity1). Asanother example, the value associated with the tiredness level may beadjusted by another amount if that day includes the high intensityactivity (Activity1) followed by another high intensity activity(Activity3).

In some embodiments, the resilience time between activities (e.g., theperiod of time of rest between Activity1 and Activity2 or Activity3) maybe weighted differently depending on the sequence of activities. Forexample, if the sequence of activities is Activity1 followed byActivity3 (e.g., two high intensity activities), the period of time maybe assigned a fifty percent higher weight than if the sequence ofactivities is Activity1 followed by Activity2. Furthermore, any activityor evidence node in a day may be adjusted based on the sequence of theactivities, which may increase accuracy of the physical predictive modelof the physical health of the patient.

In some embodiments, the probabilities associated with each state and/orthe value of the evidence nodes may be adjusted based on weather,seasons, month, or any other appropriate piece of data for the period oftime. If the current day has different weather than a previous similarday, the expected activity level may be adjusted accordingly. Forexample, if the current day has colder weather than the previous similarday, the patient may remain indoors more on the current day. ADLs and/oractivity bins that are associated with outdoor activities may beassigned a lower weight or no weight at all. Additionally, ADLs and/oractivity bins that are associated with indoor activities may be assigneda greater weight. As another example, a probability of transitioning toa node associated with a heart attack may increase during winter monthsand/or heat stroke and dehydrations may increase during summer months.

An example program in which the sequence of activities in a day arereceived and generates a tuple of activity and state for a day mayinclude:

def calculate_mood(activities_list, day): expected_array = get_baseline() actual_array = get_today_readings( ) output_lst = [ ] for i inrange(len(activities_list): x = expected_array[day][i] y =actual_array[day][i] output_lst += [(activities_lst[i], x−y) returnoutput_lst

In some embodiments, the physical predictive model may be determined asa tuple of different components that are not added together. Forexample, the physical predictive model may be equal to {SHC, CCB, LCC},in which SHC represents the short term health score, CCB represents thechronic burden score, and LCC represents the lifestyle choice score.

In some embodiments, the physical predictive model may be determined asa weighted average of the three health dimensions. For example, theweighted average of the short term health score, the chronic burdenscore, and the lifestyle choice score. Furthermore, the physicalpredictive model may be determined according to Equation 9.

HS=f(SHC,CCB,LCC)  Equation 9

In Equation 9, HS represents the health risk score, SHC represents theshort term health score, CCB represents the chronic burden score, andLCC represents the lifestyle choice score. The quantification may bebased on statistical distribution of the physical predictive model andof the big data with respect to the chronic burden score and/or thelifestyle choice score of the patient. Additionally, quantification maybe based on statistical distribution of the physical predictive modelover a baseline period for the short term health score.

The weighted average of the three health dimensions may be determinedbased on the EHR data, big data, or any other appropriate data. In someembodiments, the weighted average of the three health dimensions may beassigned by the physician of the patient. The weighted average of thethree health dimensions may incorporate a mean and a standard deviationof the short term health score, the chronic burden score, and/or thelifestyle choice score. The distribution of the sum of the short termhealth score, the chronic burden score, and the lifestyle choice scoremay be determined according to Equation 10.

V˜N(μ_(x)+μ_(y)+μ_(z),σ_(x) ²+σ_(y) ²+σ_(z) ²)  Equation 10

In Equation 10, X represents the short term health score, Y representsthe chronic burden score, Z represents the lifestyle choice score, μ_(x)represents the mean and median of the short term health score, μ_(y)represents the mean and median of the chronic burden score, μ_(z)represents the mean and median of the lifestyle choice score, arepresents the standard deviation of the short term health score, σ_(y)represents that standard deviation of the chronic burden score, andσ_(z) represents the standard deviation of the lifestyle choice score.Additionally, the weighted average of the three health dimensions may bethe same or similar. In some embodiments, the physical predictive modeland the risk of disease ρ may be determined by examining the deviationfrom the expected value.

In some embodiments, the physical predictive model may be determined byconvolving Equation 11.

V=αX+βY+γZ  Equation 11

In Equation 11, α represents the weight assigned to the short termhealth score, β represents the weight assigned to the chronic burdenscore, and γ represents the weight assigned to the lifestyle choicescore. In some embodiments, the physical predictive model determined byconvolving Equation 11 may be determined per chronic diseases. Thus thephysical predictive model of the patient experiencing multiple chronicdiseases may be determined according to Equation 12.

HS_(i) =f(SHC,CCB_(i),LCC)  Equation 12

In Equation 12, CCB_(i) may represent the chronic burden score for asingle chronic disease of the multiple chronic diseases that the patientis experiencing. Additionally, the physical predictive model may bedetermined according to Equation 13.

HS={HS₁, . . . , HS_(k)}  Equation 13

In Equation 13, each physical predictive model (e.g., HS₁ throughHS_(k)) may be either a composite health score or a triplet score of theshort term health score, the chronic burden score, and the lifestylechoice score for each chronic disease the patient is experiencing.

In some embodiments, the physical predictive model may be used toincorporate the short term health score, the chronic burden score, andthe lifestyle choice score so as to incorporate evidence nodesrepresenting the health score of the patient using patient HRQOL input,patient GSRH input, patient HALEX input, chronic burden score, and/orlifestyle choice score values.

In some embodiments, a particular day in the period of time may becompared to a similar day in another period of time. For example, if theperiod of time is a week, Mondays in the various weeks may be comparedto each other to determine whether the activity of a current Monday isdifferent from the activity level of the similar Monday in a previousperiod of time.

In some embodiments, a predictive model of the mental health of thepatient (herein ‘mental predictive model’) may be determined. The mentalpredictive model may be used to detect whether the patient is sufferingfrom heightened anxiety, a troubling down mental state, clinicaldepression, or any other neurological state. The mental predictive modelmay be determined by combining big data (e.g., CDC data) with patientdata based on the following observation: a patient in poor mental health(e.g., depressed) on a day is more likely to be in poor mental health ona subsequent day as well. In some embodiments, the mental predictivemodel may be determined daily for the period of time.

In some embodiments, the mental predictive model may determinerelationships between changes in biomarkers of the patient and themental state of the patient and/or changes in biomarkers compared to amental baseline of the patient. Additionally, the mental predictivemodel may determine deviation from the sensor data and/or deviation froma template of the patient for a day. The mental predictive model may bedetermined using a current mental state of the patient. The mentalpredictive model may be based at least in part on the sensor data. Thesensor data may be gathered by a smartphone, biosensors, varioussmartphone applications, and/or facial action coding systems (FACS).

In some embodiments, the current mental state of the patient may bedetermined based on how the patient is interacting with the smartphone.For example, the smartphone may detect a speed at which the patienttypes, how the backspace or other special symbol buttons are pressed,how much the smartphone shakes during use. Additionally, the currentstate of the patient may be determined based on the heart rate, a heartrate variance, responses from an electro dermal analysis (EDA), and/orany other appropriate biomarker of the patient.

In some embodiments, the current mental state of the patient may bedetermined based on patient input received via a smartphone application.A smartphone application, such as the Circumplex App, may include a userinterface with multiple locations representing different mental states.For example, the user interface may include locations representingpleasant activation, activated pleasure, pleasure, deactivated pleasure,pleasant deactivation, deactivation, unpleasant deactivation,deactivated displeasure, displeasure, activated displeasure, unpleasantactivation, and/or activation mental states of the patient. Furthermore,the current mental state of the patient may be determined based onemotion recognition using FACS that allows no frame to frame recordingof activity and/or life of the patient besides emotions and the activitytype. For example, a FACS may be programmed to recognize facialexpressions of the patient that are associated with different mentalstates of the patient.

In some embodiments, the mental states of the patient may be associatedwith portions of data included in the EHR data, such as BMI, bloodpressure control, smoking status, depression status, or any otherappropriate data point of the patient. Additionally, the mental statesof the patient may be based on insurance data, for example, Medicaredata, related to risk scores of the patient and/or expenses incurred dueto chronic diseases of the patient. Additionally, the mentally states ofthe patient may be based on data included in the patient HRQOL input.For example, the mental states of the patient may be based on the numberof healthy days versus the number of unhealthy days physically and/ormentally that the patient reports experiencing.

The mental states of the patient may represent the day, a mood of thepatient, an expected biomarker, and/or an external marker. The externalmarker may include states of the weather (e.g., one node may correspondto Mondays where the patient is in a Sad mental state and the weather israiny). Moods of the patient may include happy, excited, calm, normal,sad, distressed, and/or any other appropriate mood. A value of themental states may be a weighted average of deviations from the mentalbaseline of the patient. Each deviation range may be determined based ona specific probability. The mental states of the patient may representmood scores for a day.

A probability of the patient being in a particular mental state (e.g.,PO)) may be determined using mental evidence nodes. The mental evidencenodes may be based on a level of compliance of the patient with one ormore goals and/or schedule for the patient on a day. In someembodiments, the mental evidence nodes may be based on the sensor datagathered by the smartphone, biosensors, various smartphone applications,and/or a FACS. Additionally, the evidence nodes may be based on adeviation from a mean on a day. Alternatively, the mental evidence nodesmay be based on the level of compliance of the patient with one or moregoals and/or schedule for the patient during an activity. In someembodiments, the mental evidence nodes may be associated with the totalactivity level, the total sedentary time, the total sleep, the bloodpressure, the blood glucose, the pulmonary, and/or the pain level of thepatient.

Each mental evidence node may include values from the different mentalstates of the patient with a corresponding probability. For example, amental evidence node may include values from a happy node (e.g., a happymental state of the patient) with probability PH and from a sad nodewith probability P_(S). Thus, different mental evidence nodes mayinclude different distributions from which the mental evidence nodevalues are sampled. The mental evidence node values may be used todetermine the probability of the patient transitioning from one mentalstate to another mental state. For example, the probability of thepatient transitioning from a happy mental state to a sad mental state.

The value of each mental evidence node may include a distributionspecific to the patient. The distribution specific to the patient may bedetermined based on an initial mental training set of data.Additionally, the distribution specific to the patient may beextrapolated to similar patients with similar mental compositions and/ortypes. In some embodiments, the distribution specific to the patient maybe determined using empirical evidence, which may indicate a mentalbaseline of the patient for each mental evidence node (e.g., observeddeviations from an expected mean of an evidence node for each state).The comparison may be used to predict how the activity level of thepatient deviates from the expected ADLs and/or activity bins and/or topredict what the mental health of the patient is on a day.

In some embodiments, an expected number of mental evidence nodes may bedetermined for each day. The mental evidence nodes may include a valuethat represents the level of compliance with the one or more goalsand/or the schedule. For example, a mental evidence node may include ascore of one for complete compliance and a score of zero for nocompliance with the goals and/or the schedule. A mental evidence nodemay include a score between zero and one for varying levels ofcompliance with the goals and/or the schedule. In some embodiments, thevalue of each mental evidence node may be weighted based on astatistical significance of the mental evidence node.

A range of values for the mental evidence nodes may be assigned with aprobability based on the mental baseline of the patient. For example,empirically determined probabilities for event A and event B may bedetermined and assigned. The probability of the range of values may bedetermined by dividing a number of times an event occurs by a totalnumber of times events occur.

Activity levels of the patient may be determined each day during theperiod of time. Likewise, the mental state of the patient may bedetermined each day. Furthermore, activity levels and/or mental state ofthe patient may be determined during each activity.

A probability of the patient transitioning from a current mental state(e.g., π_(t)) on one day to another mental state (e.g., π_(t)′) on asubsequent day may be determined (e.g., P(π_(t)|π_(t)′)). A probabilityof the patient being in one mental state may be determined usingEquation 14.

P(π_(t) |E _(day 1) , E _(day 2) , . . . , E _(day n)))  Equation 14

In Equation 14, E_(day 1) through E_(day n) may represent the mentalevidence nodes for each day of the period of time.

In an example in which the patient may be in one of two states (e.g., ahappy mental state or a sad mental state) on a day, the probability ofthe patient being in the sad mental state may be determined usingconditional probability. The probability of the patient being in the sadmental state given N evidence nodes may be determined according toEquation 15.

P(π_(t) =Sad|E _(Day 1) ,E _(Day 2) , . . . , E _(Day N))=[P _(S)*(Π^(n)_(i=1) P(E _(Day(i)) |E _(Day(i−1)) ,E _(Day(i−2)) , . . . , E_(Day1),Sick))]/P(E _(Day 1) , . . . , E _(Day N))=[P _(S)*(Π^(n) _(i=1)P(E _(Day(i)) |E _(Day(i+1)) ,E _(Day(i+2)) , . . . , E_(DayN),Sick))]/P(E _(Day 1) , . . . , E _(Day N))  Equation 15

In Equation 15, P_(S) may represent the probability that the patient isin the sad mental state.

In another example in which the patient may be in one of three mentalstates (e.g., the happy mental state, an intermediate mental state, orthe sad mental state) on a day, the probability of the patient being inone of the three mental states given N mental evidence nodes mayinitially be determined according to Equation 14. The probability of thepatient being in one of the three mental states may be marginalized overall three mental states, since the mental evidence nodes may includeconditional independence according to Equation 3. The significance levelof the current mental state may be determined recursively usingEquations 4, 5, and 6. Based on Equations 4, 5, and 6, the probabilityof the patient being in the current mental state on a day given themental evidence node values may be determined according to Equation 7.

An example program in which the probability of the patient being happyor sad, given the mental evidence nodes over that last N days; thedistribution of the mental states of the patient; the probability oftransitioning between each mental state; the mental baseline of thepatient; and a model setup may include:

function calculate_current_state(n_observations, day): markov_model =new Model(transition_probabilities, distribution_per_node)possible_states = markov_model.get_possible_states(day)today_observation = getTodaysData( ) state_probabilities = [ ] for statein possible_states:prob=markov_model.get_probability_of_state(today_observation,n_observations)state_probabilities += [(state, prob)] return(argmax(state_probabilities), state_probabilities)In the example program, the function get_probability_of_state( ) maydetermine (Pπ_(t)|E_(Day 1), E_(Day 2), . . . , E_(Day N)) for eachpossible mental state of the patient. Additionally, the functionget_probability_of_state( ) may return a tuple of the most likely stateusing distribution over all mental states.

In some embodiments, a metric of emotion may be determined by creatingmood tuples (e.g., (mood, activity)) for each activity in a day and byusing mental evidence nodes for each activity rather than each day. Thepatient may receive feedback on the prediction of the mood of thepatient after performing the activity.

In some embodiments, the probabilities associated with each mental stateand/or the value of the mental evidence nodes may be adjusted based onweather, seasons, month, or any other appropriate piece of data for theperiod of time. For example, if a current mental state of the patient isdepressed, the probability of the mental state of the patient beingdepressed may increase. Thus, the probability of the patienttransitioning from a depressed mental state on one day to a depressedmental state on a subsequent day may be increased.

In some embodiments, a predicted distribution of the sensor data may bedetermined based on a single mental state of the patient and the mentaltraining set of data. For example, the single mental state may include:Monday, Raining, Mood=5.0/10.0. The predicted distribution of the sensordata may be determined to correspond to the single node with specificprobabilities.

In some embodiments, the mental state of the patient may be determinedbased on the day and the activity only. The day and the activity may besufficient to determine the mental state if the patient typicallyfollows a regimented schedule, is not clinically depressed, and does notsuffer from other neurological disorders. A deviation from the mean ofthe expected activity of the patient may be used to determine the mentalstate. Determining the mental state based on the day and the activityonly may provide data indicating an impact an activity may have on themental state of the patient.

An example program in which the sequence of activities in a day arereceived and generates a tuple of activity and state for a day mayinclude:

def calculate_mood(activities_list, day): expected_array = get_baseline() actual_array = get_today_readings( ) output_lst = [ ] for i inrange(len(activities_list): x = expected_array[day][i] y =actual_array[day][i] output_lst += [(activities_lst[i], x−y) returnoutput_lst

In some embodiments, the mental predictive model may be determined as atuple of different components that are not added together. For example,the mental predictive model may be equal to (SHC, CCB, LCC, SMC), inwhich SHC represents the short term health score, CCB represents thechronic burden score, LCC represents the lifestyle choice score, and SMCrepresents the short term mood change.

FIG. 12 is a block diagram illustrating a physical predictive model 1200of the physical health of a patient in which the patient may be in oneof two states. The physical predictive model 1200 may include a firststate 1221 a of the patient, a second state 1221 b of the patient, athird state 1221 c of the patient, and a fourth state 1221 d of thepatient (collectively referred to herein as the states 1221). Thephysical predictive model 1200 may also include a first evidence node1223 a and a second evidence node 1223 b (collectively referred toherein as the evidence nodes 1223 or evidence node 1223). The physicalpredictive model 1200 may be generated as discussed elsewhere herein.

As illustrated in FIG. 12, the first evidence node 1223 a may correspondto a Monday during a period of time (e.g., a first day). Likewise, thesecond evidence node 1223 b may correspond to a Tuesday during theperiod of time (e.g., a second day). It is understood that the evidencenodes 1223 may correspond to different days in the period of time. Theevidence nodes 1223 may be based on a level of compliance of the patientwith one or more goals and/or schedule for the patient on a day asdiscussed elsewhere.

As illustrated in FIG. 12, the first state 1221 a may correspond to thepatient being in a healthy state on the first day. Additionally, asillustrated in FIG. 12, the second state 1221 b may correspond to thepatient being in a sick state on the first day. Also, as illustrated inFIG. 12, the third state 1221 c may correspond to the patient being in ahealthy state on the second day. Furthermore, as illustrated in FIG. 12,the fourth state 1221 d may correspond to the patient being in a sickstate on the second day. It is to be understood that the states 1221 maycorrespond to different physical states of the patient.

A probability of the patient being in each of the states 1221 may bedetermined using Equation 2. The probability of the patient being in oneof the states 1221 are represented as PH and P_(S) in FIG. 12. PH mayrepresent the probability of the patient being in the healthy state andP_(S) may represent the probability of the patient being the sick state.For example, PH may represent the probability of the patient being inthe first state 1221 a on the first day and/or the third state 1221 c onthe second day. As another example, P_(S) may represent the probabilityof the patient being in the second state 1221 b on the first day and/orthe fourth state 1221 d on the second day.

A probability of the patient transitioning from one state 1221 on thefirst day to another state 1221 on the second day may be determined asdiscussed elsewhere. The probability of the patient transitioning fromone state 1221 on the first day to another state 1221 on the second dayare represented as P_(W), P_(X), P_(Y), and P_(Z) in FIG. 12. Forexample, P_(W) may represent the probability of the patienttransitioning from the first state 1221 a on the first day to the thirdstate 1221 c on the second day. As another example, P_(X) may representthe probability of the patient transitioning from the first state 1221 aon the first day to the fourth state 1221 d on the second day. As yetanother example, P_(Y) may represent the probability of the patienttransitioning from the second state 1221 b on the first day to the thirdstate 1221 c on the second day. For example, P_(Z) may represent theprobability of the patient transitioning from the second state 1221 b onthe first day to the fourth state 1221 d on the second day.

FIG. 13 is another block diagram of a physical predictive model 1300 ofthe physical health of a patient in which the patient may be in one ofthree states. The physical predictive model 1300 may include a firststate 1321 a of the patient, a second state 1321 b of the patient, athird state 1321 c of the patient, a fourth state 1321 d of the patient,a fifth state 1321 e of the patient, and a sixth state 1321 f of thepatient (collectively referred to herein as the states 1321). Thephysical predictive model 1300 may also include a first evidence node1323 a and a second evidence node 1323 b (collectively referred toherein as the evidence nodes 1323 or evidence node 1323). The physicalpredictive model 1300 may be generated as discussed elsewhere herein.

As illustrated in FIG. 13, the first evidence node 1323 a may correspondto a Monday during a period of time (e.g., a first day). Likewise, thesecond evidence node 1323 b may correspond to a Tuesday during theperiod of time (e.g., a second day). It is understood that the evidencenodes 1323 may correspond to different days in the period of time. Theevidence nodes 1323 may be based on a level of compliance of the patientwith one or more goals and/or schedule for the patient on a day asdiscussed elsewhere.

As illustrated in FIG. 13, the first state 1321 a may correspond to thepatient being in a healthy state on the first day. Additionally, asillustrated in FIG. 13, the second state 1321 b may correspond to thepatient being in an intermediate state on the first day. Also, asillustrated in FIG. 13, the third state 1321 c may correspond to thepatient being in a sick state on the first day. Furthermore, asillustrated in FIG. 13, the fourth state 1321 d may correspond to thepatient being in a healthy state on the second day. As illustrated inFIG. 13, the fifth state 1321 e may correspond to the patient being inan intermediate state on the second day. Also, as illustrated in FIG.13, the sixth state 1321 f may correspond to the patient being in a sickstate on the second day. It is to be understood that the states 1321 maycorrespond to different physical states of the patient.

A probability of the patient being in each of the states 1321 may bedetermined using Equation 2. The probability of the patient being in oneof the states 1321 are represented as P_(H), P_(I), and P_(S) in FIG.13. PH may represent the probability of the patient being in the healthystate. P_(I) may represent the probability of the patient being in theintermediate state. P_(S) may represent the probability of the patientbeing in the sick state. For example, PH may represent the probabilityof the patient being in the first state 1321 a on the first day and/orthe fourth state 1321 d on the second day. As another example, P_(I) mayrepresent the probability of the patient being in the second state 1321b on the first day and/or the fifth state 1321 e on the second day. Asyet another example, P_(S) may represent the probability of the patientbeing in the third state 1321 c on the first day and/or the sixth state1321 f on the second day.

A probability of the patient transitioning from one state 1321 on thefirst day to another state 1321 on the second day may be determined asdiscussed elsewhere. The probability of the patient transitioning fromone state 1321 on the first day to another state 1321 on the second dayare represented as P_(A), P_(B), P_(C), P_(D), P_(L), P_(W), P_(X),P_(Y), and P_(Z) in FIG. 13. For example, P_(L) may represent theprobability of the patient transitioning from the first state 1321 a onthe first day to the fifth state 1321 e on the second day. As anotherexample, P_(X) may represent the probability of the patienttransitioning from the first state 1321 a on the first day to the sixthstate 1321 f on the second day. As yet another example, P_(Z) mayrepresent the probability of the patient transitioning from the firststate 1321 a on the first day to the fourth state 1321 d on the secondday.

Likewise, P_(A) may represent the probability of the patienttransitioning from the second state 1321 b on the first day to thefourth state 1321 d on the second day. Also, P_(B) may represent theprobability of the patient transitioning from the second state 1321 b onthe first day to the fifth state 1321 e on the second day. Furthermore,P_(D) may represent the probability of the patient transitioning fromthe second state 1321 b on the first day to the sixth state 1321 f onthe second day.

Additionally, P_(W) may represent the probability of the patienttransitioning from the third state 1321 c on the first day to the sixthstate 1321 f on the second day. Also, P_(Y) may represent theprobability of the patient transitioning from the third state 1321 c onthe first day to the fourth state 1321 d on the second day. Likewise,P_(C) may represent the probability of the patient transitioning fromthe third state 1321 c on the first day to the fifth state 1321 e on thesecond day

FIG. 14 is a block diagram of an example of the computing device 1400,arranged in accordance with at least one embodiment of the presentdisclosure. The computing device 1400 may be used in some embodiments toperform or control performance of one or more of the methods and/oroperations described herein. For instance, the computing device 1400 maybe communicatively coupled to and/or included in or as the electronicdevice 102 described in relation to FIG. 1 to perform or controlperformance of the methods 200, 700, 800, 900, 1000, and 1100 of FIGS.2, 8, 9, 10, and 11. In a basic configuration 1402, the computing device1400 typically includes one or more processors 1404 and a system memory1406. A memory bus 1408 may be used for communicating between theprocessor 1404 and the system memory 1406.

Depending on the desired configuration, the processor 1404 may be of anytype including, such as a microprocessor (μ³), a microcontroller (μC), adigital signal processor (DSP), or any combination thereof. Theprocessor 1404 may include one or more levels of caching, such as alevel one cache 1410 and a level two cache 1412, a processor core 1414,and registers 1416. The processor core 1414 may include an arithmeticlogic unit (ALU), a floating point unit (FPU), a digital signalprocessing core (DSP Core), or any combination thereof. An examplememory controller 1418 may also be used with the processor 1404, or insome implementations the memory controller 1418 may be an internal partof the processor 1404.

Depending on the desired configuration, the system memory 1406 may be ofany type, such as volatile memory (such as RAM), non-volatile memory(such as ROM, flash memory, or the like), or any combination thereof.The system memory 1406 may include an operating system 1420, one or moreapplications 1422, and program data 1424. The application 1422 mayinclude a chronic disease algorithm 1426 that is arranged to predict alikelihood of a patient experiencing an acute event in the near future,evaluate relative risk of under-diagnosis of a patient, evaluate andstratify a chronic care burden of a patient, generate a health riskscore of a patient, and/or evaluate and stratify a lifestyle healthcompliance of a patient as described herein. The program data 1424 mayinclude chronic disease data 1428 such as chronic data and/or EHR datathat may be used to control aspects of the methods and/or operationsdescribed herein. In some embodiments, the application 1422 may bearranged to operate with the program data 1424 on the operating system1420 to perform one or more of the methods and/or operations describedherein, including those described with respect to FIGS. 2 and 7-11.

The computing device 1400 may include additional features orfunctionality, and additional interfaces to facilitate communicationsbetween the basic configuration 1402 and any other devices andinterfaces. For example, a bus/interface controller 1430 may be used tofacilitate communications between the basic configuration 1402 and oneor more data storage devices 1432 via a storage interface bus 1434. Thedata storage devices 1432 may include removable storage devices 1436,non-removable storage devices 1438, or a combination thereof. Examplesof removable storage and non-removable storage devices include magneticdisk devices such as flexible disk drives and hard-disk drives (HDDs),optical disk drives such as compact disk (CD) drives or digitalversatile disk (DVD) drives, solid state drives (SSDs), and tape drivesto name a few. Example computer storage media may include volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information, such as computer-readableinstructions, data structures, program modules, or other data.

The system memory 1406, the removable storage devices 1436, and thenon-removable storage devices 1438 are examples of computer storagemedia. Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVDs) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which may be used to store the desired informationand which may be accessed by the computing device 1400. Any suchcomputer storage media may be part of the computing device 1400.

The computing device 1400 may also include an interface bus 1440 forfacilitating communication from various interface devices (e.g., outputdevices 1442, peripheral interfaces 1444, and communication devices1446) to the basic configuration 1402 via the bus/interface controller1430. The output devices 1442 include a graphics processing unit 1448and an audio processing unit 1450, which may be configured tocommunicate to various external devices such as a display or speakersvia one or more A/V ports 1452. The peripheral interfaces 1444 include aserial interface controller 1454 or a parallel interface controller1456, which may be configured to communicate with external devices suchas input devices (e.g., keyboard, mouse, pen, voice input device, touchinput device, and/or others), sensors, or other peripheral devices(e.g., printer, scanner, and/or others) via one or more I/O ports 1458.The communication devices 1446 include a network controller 1460, whichmay be arranged to facilitate communications with one or more othercomputing devices 1462 over a network communication link via one or morecommunication ports 1464.

FIG. 15 is a flow diagram of an example method 1500 to generate aphysical predictive model of a physical health of a patient, arranged inaccordance with at least one embodiment described herein. The method1500 may be performed by a computer such as the computing device 1400 ofFIG. 14. The method 1500 may be performed, in whole or in part, by thecomputing device. Alternatively or additionally, the method 1500 may beimplemented by a processor device that performs or controls performanceof one or more of the operations of the method 1500.

The method 1500 may include one or more of blocks 1502, 1504, 1506,1508, 1510, 1512, and/or 1514. Although illustrated as discrete blocks,various blocks may be divided into additional blocks, supplemented withadditional blocks, combined into fewer blocks, or eliminated, dependingon the particular implementation. The method 1500 may begin at block1502.

In block 1502 (“Collect Sensor Data Related To A Physical Health Of APatient”), sensor data related to a physical health of a patient may becollected. In some embodiments, the sensor data may be collected by thecomputing device from the sensors. In these and other embodiments, thesensor data may pertain to, include, and/or indicate at least one of adiet pattern, a sleep pattern, an exercise pattern, an activity level,heart rate, posture, stress, blood pressure variation, blood glucose,heart rhythm, smoking status, pain level, and/or GPS data of thepatient. Additionally or alternatively, the sensor data may be relatedto a mental health of the patient. Block 1502 may be followed by block1504.

In block 1504 (“Determine A Series Of Evidence Nodes”), a series ofphysical evidence nodes may be determined. The physical evidence nodesmay correspond with at least one of a schedule and one or more goals forthe patient during a single day within a period of time. Block 1504 maybe followed by block 1506.

In block 1506 (“Determine A Series Of States Of The Patient”), a seriesof physical states of the patient may be determined. Each physical stateof the patient may be associated with a single physical evidence node.Block 1506 may be followed by block 1508.

In block 1508 (“Determine An Expected Value For Each Evidence Node”) anexpected value for each physical evidence node may be determined. Theexpected value may be determined based on a physical baseline of thepatient. Block 1508 may be followed by block 1510.

In block 1510 (“Determine, After Each Day, A Value For EachCorresponding Physical Evidence Node”), a value for each correspondingphysical evidence node may be determined after each day. Block 1510 maybe followed by block 1512.

In block 1512 (“Determine, After Each Day, A Deviation Of The PatientFrom The Expected Value For Each Corresponding Physical Evidence Node”),a deviation of the patient from the expected value for eachcorresponding physical evidence node may be determined after each day.Block 1512 may be followed by block 1514.

In block 1514 (“Generate A Physical Predictive Model Of The PhysicalHealth Of The Patient”), a physical predictive model of the physicalhealth of the patient may be generated. The physical predictive model ofthe physical health of the patient may be generated based on thedeviation of the patient from the expected value for each correspondingphysical evidence node.

FIG. 16 is a flow diagram of an example method 1600 to generate a mentalpredictive model of a mental health of a patient, arranged in accordancewith at least one embodiment described herein. The method 1600 may beperformed by a computer such as the computing device 1400 of FIG. 14.The method 1600 may be performed, in whole or in part, by the computingdevice. Alternatively or additionally, the method 1600 may beimplemented by a processor device that performs or controls performanceof one or more of the operations of the method 1600.

The method 1600 may include one or more of blocks 1602, 1604, 1606,1608, 1610, 1612, and/or 1614. Although illustrated as discrete blocks,various blocks may be divided into additional blocks, supplemented withadditional blocks, combined into fewer blocks, or eliminated, dependingon the particular implementation. The method 1600 may begin at block1602.

In block 1602 (“Collect Sensor Data Related To A Mental Health Of APatient”), sensor data related to a mental health of a patient may becollected. In some embodiments, the sensor data may be collected by thecomputing device from the sensors. In these and other embodiments, thesensor data may pertain to, include, and/or indicate at least one of adiet pattern, a sleep pattern, an exercise pattern, an activity level,heart rate, posture, stress, blood pressure variation, blood glucose,heart rhythm, smoking status, pain level, and/or GPS data of thepatient. Additionally or alternatively, the sensor data may be relatedto a mental health of the patient. Block 1602 may be followed by block1604.

In block 1604 (“Determine A Series Of Mental Evidence Nodes”), a seriesof mental evidence nodes may be determined. The mental evidence nodesmay correspond with at least one of a schedule and one or more goals forthe patient during a single day within a period of time. Block 1604 maybe followed by block 1606.

In block 1606 (“Determine A Series Of Mental States Of The Patient”), aseries of mental states of the patient may be determined. Each mentalstate of the patient may be associated with a single mental evidencenode. Block 1606 may be followed by block 1608.

In block 1608 (“Determine An Expected Value For Each Mental EvidenceNode”) an expected value for each mental evidence node may bedetermined. The expected value may be determined based on a mentalbaseline of the patient. Block 1608 may be followed by block 1610.

In block 1610 (“Determine, After Each Day, A Value For EachCorresponding Mental Evidence Node”), a value for each correspondingmental evidence node may be determined after each day. Block 1610 may befollowed by block 1612.

In block 1612 (“Determine, After Each Day, A Deviation Of The PatientFrom The Expected Value For Each Corresponding Mental Evidence Node”), adeviation of the patient from the expected value for each correspondingmental evidence node may be determined after each day. Block 1612 may befollowed by block 1614.

In block 1614 (“Generate A Mental Predictive Model Of The Mental HealthOf The Patient”), a mental predictive model of the mental health of thepatient may be generated. The mental predictive model of the mentalhealth of the patient may be generated based on the deviation of thepatient from the expected value for each corresponding mental evidencenode.

The network communication link may be one example of a communicationmedia. Communication media may typically be embodied bycomputer-readable instructions, data structures, program modules, orother data in a modulated data signal, such as a carrier wave or othertransport mechanism, and may include any information delivery media. A“modulated data signal” may be a signal that includes one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia may include wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, radio frequency (RF),microwave, infrared (IR), and other wireless media. The term“computer-readable media” as used herein may include both storage mediaand communication media.

The computing device 1400 may be implemented as a portion of asmall-form factor portable (or mobile) electronic device such as a cellphone, a personal data assistant (PDA), a personal media player device,a wireless web-watch device, a personal headset device, anapplication-specific device, or a hybrid device that include any of theabove functions. The computing device 1400 may also be implemented as apersonal computer including both laptop computer and non-laptop computerconfigurations.

The present disclosure is not to be limited in terms of the particularembodiments described herein, which are intended as illustrations ofvarious aspects. Many modifications and variations can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. Functionally equivalent methods and apparatuseswithin the scope of the disclosure, in addition to those enumeratedherein, will be apparent to those skilled in the art from the foregoingdescriptions. Such modifications and variations are intended to fallwithin the scope of the appended claims. The present disclosure is to belimited only by the terms of the appended claims, along with the fullscope of equivalents to which such claims are entitled. It is to beunderstood that the present disclosure is not limited to particularmethods, reagents, compounds, compositions, or biological systems, whichcan, of course, vary. It is also to be understood that the terminologyused herein is for the purpose of describing particular embodimentsonly, and is not intended to be limiting.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed is:
 1. A system to evaluate relative risk ofunder-diagnosis of a patient, the system comprising: a first databaseincluding data that indicates a number of patients that are diagnosedwith one or more chronic diseases in a geographic location based onepidemiological attributes of the patients; a second database includingdata that indicates a number of patients that are diagnosed with the oneor more chronic diseases in a practice of a physician; a memoryconfigured to store the first database and the second database; and aprocessor, coupled to the memory, wherein the processor is configured toperform executable operations including: compare the number of thepatients diagnosed with the one or more chronic diseases in the practiceof the physician included in the second database to the number ofpatients diagnosed with the one or more chronic diseases in thegeographic location included in the first database; generate acomparison result, wherein the comparison result indicates whether thenumber of patients diagnosed with the one or more chronic diseases inthe practice of the physician is greater than or less than the number ofpatients diagnosed with the one or more chronic diseases in thegeographic location; and provide an alert to a care-provider, whereinthe alert includes the comparison result.
 2. The system of claim 1,wherein the comparison result is based on the comparison of the numberof patients diagnosed with the one or more chronic diseases in thepractice of the physician included in the second database to the numberof patients diagnosed with the one or more chronic diseases in thegeographic location included in the first database.
 3. The system ofclaim 1, wherein the number of patients diagnosed with one or morechronic diseases in the geographic location is adjusted based onstatistical data included in the first database that indicatesepidemiological categorization of one or more patient attributes of thepatients including at least one of an age, an ethnicity, a gender, anincome level, and an education level of the patients.
 4. The system ofclaim 1, wherein the second database also includes data that indicates anumber of patients in the practice of the physician that have undergonelaboratory based screening test for the one or more chronic diseases andthe executable operations further comprising compare the number ofpatients diagnosed with the one or more chronic diseases in thegeographic location included in the first database to the number ofpatients in the practice of the physician that have undergone laboratorybased screening test for the one or more chronic diseases included inthe second database and the comparison result is further based on thecomparison of the number of patients diagnosed with the one or morechronic diseases in the geographic location to the number of patients inthe practice of the physician that have undergone laboratory basedscreening test for the one or more chronic diseases.
 5. The system ofclaim 1, wherein the second database also includes data that indicates anumber of patients in the practice of the physician that have beenstratified as having a higher risk for the one or more chronic diseasesbased on a physician annotated diagnosis and the executable operationsfurther comprising compare the number of patients diagnosed with the oneor more chronic diseases in the geographic location included in thefirst database to the number of patients in the practice of thephysician that have been stratified as having a higher risk for the oneor more chronic diseases based on the physician annotated diagnosisincluded in the second database and the comparison result is furtherbased on the comparison of the number of patients diagnosed with the oneor more chronic diseases in the geographic location to the number ofpatients in the practice of the physician that have been stratified ashaving a higher risk for the one or more chronic diseases based on thephysician annotated diagnosis.
 6. The system of claim 1, wherein thesecond database also includes data that indicates a number of patientsin the practice of the physician that have been stratified as having ahigher risk for the one or more chronic diseases by a computer baseddisease calculator and the executable operations further comprisingcompare the number of patients diagnosed with the one or more chronicdiseases in the geographic location included in the first database tothe number of patients in the practice of the physician that have beenstratified as having a higher risk for the one or more chronic diseasesby the computer based disease calculator included in the second databaseand the comparison result is further based on the comparison of thenumber of patients diagnosed with the one or more chronic diseases inthe geographic location to the number of patients in the practice of thephysician that have been stratified as having a higher risk for the oneor more chronic diseases by the computer based disease calculator. 7.The system of claim 1, wherein the second database also includes datathat indicates a number of patients in the practice of the physicianwith a higher risk for the one or more chronic diseases using sensordata related to a quality of health of the patients and the sensor datarelated to a quality of health of the patients is based on at least oneof a diet pattern, a sleep pattern, an exercise pattern, an activitylevel, a heart rate, a posture, a stress, a blood pressure variation, ablood glucose, a heart rhythm, a smoking status, a pain level, a mooddata, and a GPS data.
 8. The system of claim 1, wherein the seconddatabase also includes data that indicates a number of patients in thepractice of the physician with poor compliance to medicalrecommendations by the physician and the executable operations furthercomprising compare the number of patients diagnosed with the one or morechronic diseases in the geographic location included in the firstdatabase to the number of patients in the practice of the physician withpoor compliance to the medical recommendations by the physician includedin the second database and the comparison result is further based on thecomparison of the number of patients diagnosed with the one or morechronic diseases in the geographic location to the number of patients inthe practice of the physician with poor compliance to the medicalrecommendations by the physician.
 9. The system of claim 1, wherein thefirst database also includes data that indicates population norms andexpected deviation for the one or more chronic diseases for short-termhealth related quality of life (HRQOL) using a health and activitieslimitation index (HALex) and the executable operations furthercomprising compare the population norms and expected deviation for theone or more chronic diseases for short-term HRQOL using the HALexincluded in the first database to the number of the patients diagnosedwith the one or more chronic diseases in the practice of the physicianincluded in the second database and the comparison result is furtherbased on the comparison of the population norms and expected deviationfor the one or more chronic diseases for short-term HRQOL using theHALex to the number of the patients diagnosed with the one or morechronic diseases in the practice of the physician.
 10. The system ofclaim 1, wherein the first database also includes data that indicatespopulation norms and expected deviation for the one or more chronicdiseases for short-term HRQOL using a visual analog scale (VAS) HRQOLscore and the executable operations further comprising compare thepopulation norms and expected deviation for the one or more chronicdiseases for short-term HRQOL using the VAS HRQOL score included in thefirst database to the number of the patients diagnosed with the one ormore chronic diseases in the practice of the physician included in thesecond database and the comparison result is further based on thecomparison of the population norms and expected deviation for the one ormore chronic diseases for short-term HRQOL using the VAS HRQOL score tothe number of the patients diagnosed with the one or more chronicdiseases in the practice of the physician.
 11. A method to evaluaterelative risk of under-diagnosis of a patient, the method comprising:collecting data that indicates a number of patients diagnosed with oneor more chronic diseases in a geographic location based onepidemiological attributes of the patients; collecting data thatindicates a number of patients that are diagnosed with the one or morechronic diseases in a practice of a physician; comparing the number ofthe patients diagnosed with the one or more chronic diseases in thepractice of the physician to the number of patients diagnosed with theone or more chronic diseases in the geographic location; generating acomparison result, wherein the comparison result indicates whether thenumber of patients diagnosed with the one or more chronic diseases inthe practice of the physician is greater than or less than the number ofpatients diagnosed with the one or more chronic diseases in thegeographic location; and providing an alert to a care-provider, whereinthe alert includes the comparison result.
 12. The method of claim 11,wherein the comparison result is based on the comparison of the numberof patients diagnosed with the one or more chronic diseases in thepractice of the physician to the number of patients diagnosed with theone or more chronic diseases in the geographic location.
 13. The methodof claim 11, the method further comprising adjusting the number ofpatients diagnosed with one or more chronic diseases in the geographiclocation based on statistical data that indicates epidemiologicalcategorization of one or more patient attributes of the patientsincluding at least one of an age, an ethnicity, a gender, an incomelevel, and an education level of the patients.
 14. The method of claim11, the method further comprising: collecting data that indicates anumber of patients in the practice of the physician that have undergonelaboratory based screening test for the one or more chronic diseases;and comparing the number of patients diagnosed with the one or morechronic diseases in the geographic location to the number of patients inthe practice of the physician that have undergone laboratory basedscreening test for the one or more chronic diseases, wherein thecomparison result is further based on the comparison of the number ofpatients diagnosed with the one or more chronic diseases in thegeographic location to the number of patients in the practice of thephysician that have undergone laboratory based screening test for theone or more chronic diseases.
 15. The method of claim 11, the methodfurther comprising: collecting data that indicates a number of patientsin the practice of the physician that have been stratified as having ahigher risk for the one or more chronic diseases based on a physicianannotated diagnosis; and comparing the number of patients diagnosed withthe one or more chronic diseases in the geographic location to thenumber of patients in the practice of the physician that have beenstratified as having a higher risk for the one or more chronic diseasesbased on the physician annotated diagnosis, wherein the comparisonresult is further based on the comparison of the number of patientsdiagnosed with the one or more chronic diseases in the geographiclocation to the number of patients in the practice of the physician thathave been stratified as having a higher risk for the one or more chronicdiseases based on the physician annotated diagnosis.
 16. The method ofclaim 11, the method further comprising: collecting data that indicatesa number of patients in the practice of the physician that have beenstratified as having a higher risk for the one or more chronic diseasesby a computer based disease calculator; and comparing the number ofpatients diagnosed with the one or more chronic diseases in thegeographic location to the number of patients in the practice of thephysician that have been stratified as having a higher risk for the oneor more chronic diseases by the computer based disease calculator,wherein the comparison result is further based on the comparison of thenumber of patients diagnosed with the one or more chronic diseases inthe geographic location to the number of patients in the practice of thephysician that have been stratified as having a higher risk for the oneor more chronic diseases by the computer based disease calculator. 17.The method of claim 11, the method further comprising: collecting sensordata related to a quality of health of the patients, the sensor datarelated to a quality of health of the patients is based on at least oneof a diet pattern, a sleep pattern, an exercise pattern, an activitylevel, a heart rate, a posture, a stress, a blood pressure variation, ablood glucose, a heart rhythm, a smoking status, a pain level, a mooddata, and a GPS data; and generating data that indicates a number ofpatients in the practice of the physician with a higher risk for the oneor more chronic diseases based on the sensor data.
 18. The method ofclaim 11, the method further comprising: collecting data that indicatesa number of patients in the practice of the physician with poorcompliance to medical recommendations by the physician; and comparingthe number of patients diagnosed with the one or more chronic diseasesin the geographic location to the number of patients in the practice ofthe physician with poor compliance to the medical recommendations by thephysician, wherein the comparison result is further based on thecomparison of the number of patients diagnosed with the one or morechronic diseases in the geographic location to the number of patients inthe practice of the physician with poor compliance to the medicalrecommendations by the physician.
 19. The method of claim 11, the methodfurther comprising: collecting data that indicates population norms andexpected deviation for the one or more chronic diseases for short-termHRQOL using a HALex; and comparing the population norms and expecteddeviation for the one or more chronic diseases for short-term HRQOLusing the HALex to the number of the patients diagnosed with the one ormore chronic diseases in the practice of the physician, wherein thecomparison result is further based on the comparison of the populationnorms and expected deviation for the one or more chronic diseases forshort-term HRQOL using the HALex to the number of the patients diagnosedwith the one or more chronic diseases in the practice of the physician.20. The method of claim 11, the method further comprising: collectingdata that indicates population norms and expected deviation for the oneor more chronic diseases for short-term HRQOL using a VAS HRQOL score;and comparing the population norms and expected deviation for the one ormore chronic diseases for short-term HRQOL using the VAS HRQOL score tothe number of the patients diagnosed with the one or more chronicdiseases in the practice of the physician, wherein the comparison resultis further based on the comparison of the population norms and expecteddeviation for the one or more chronic diseases for short-term HRQOLusing the VAS HRQOL score to the number of the patients diagnosed withthe one or more chronic diseases in the practice of the physician.